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Rutgers University

1.
Ahmed, Talal, 1990-.
Geometric *manifold* approximation using locally linear approximations.

Degree: MS, Electrical and Computer Engineering, 2016, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/49901/

► The design and analysis of methods in signal processing is greatly impacted by the model being selected to represent the signals of interest. For many…
(more)

Subjects/Keywords: Manifold (Learning theory)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Ahmed, Talal, 1. (2016). Geometric manifold approximation using locally linear approximations. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/49901/

Chicago Manual of Style (16^{th} Edition):

Ahmed, Talal, 1990-. “Geometric manifold approximation using locally linear approximations.” 2016. Masters Thesis, Rutgers University. Accessed February 22, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/49901/.

MLA Handbook (7^{th} Edition):

Ahmed, Talal, 1990-. “Geometric manifold approximation using locally linear approximations.” 2016. Web. 22 Feb 2020.

Vancouver:

Ahmed, Talal 1. Geometric manifold approximation using locally linear approximations. [Internet] [Masters thesis]. Rutgers University; 2016. [cited 2020 Feb 22]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49901/.

Council of Science Editors:

Ahmed, Talal 1. Geometric manifold approximation using locally linear approximations. [Masters Thesis]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49901/

2.
田中, 大介.
*Manifold**Learning* from High-Dimensional Data for System Modeling, Prediction and Robot Tactile Perception : システムモデリング・予測・ロボットの触知覚のための高次元データからの多様体学習; システム モデリング ヨソク ロボット ノ ショクチカク ノ タメ ノ コウジゲン データ カラ ノ タヨウタイ ガクシュウ.

Degree: 博士(工学), Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/10610

Subjects/Keywords: Manifold Learning

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APA (6^{th} Edition):

田中, . (n.d.). Manifold Learning from High-Dimensional Data for System Modeling, Prediction and Robot Tactile Perception : システムモデリング・予測・ロボットの触知覚のための高次元データからの多様体学習; システム モデリング ヨソク ロボット ノ ショクチカク ノ タメ ノ コウジゲン データ カラ ノ タヨウタイ ガクシュウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/10610

Note: this citation may be lacking information needed for this citation format:

No year of publication.

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

田中, 大介. “Manifold Learning from High-Dimensional Data for System Modeling, Prediction and Robot Tactile Perception : システムモデリング・予測・ロボットの触知覚のための高次元データからの多様体学習; システム モデリング ヨソク ロボット ノ ショクチカク ノ タメ ノ コウジゲン データ カラ ノ タヨウタイ ガクシュウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 22, 2020. http://hdl.handle.net/10061/10610.

Note: this citation may be lacking information needed for this citation format:

No year of publication.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

田中, 大介. “Manifold Learning from High-Dimensional Data for System Modeling, Prediction and Robot Tactile Perception : システムモデリング・予測・ロボットの触知覚のための高次元データからの多様体学習; システム モデリング ヨソク ロボット ノ ショクチカク ノ タメ ノ コウジゲン データ カラ ノ タヨウタイ ガクシュウ.” Web. 22 Feb 2020.

Note: this citation may be lacking information needed for this citation format:

No year of publication.

Vancouver:

田中 . Manifold Learning from High-Dimensional Data for System Modeling, Prediction and Robot Tactile Perception : システムモデリング・予測・ロボットの触知覚のための高次元データからの多様体学習; システム モデリング ヨソク ロボット ノ ショクチカク ノ タメ ノ コウジゲン データ カラ ノ タヨウタイ ガクシュウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 22]. Available from: http://hdl.handle.net/10061/10610.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

No year of publication.

Council of Science Editors:

田中 . Manifold Learning from High-Dimensional Data for System Modeling, Prediction and Robot Tactile Perception : システムモデリング・予測・ロボットの触知覚のための高次元データからの多様体学習; システム モデリング ヨソク ロボット ノ ショクチカク ノ タメ ノ コウジゲン データ カラ ノ タヨウタイ ガクシュウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/10610

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

No year of publication.

UCLA

3.
Flynn, John Joseph.
* Learning* a simplicial structure using sparsity.

Degree: Statistics, 2014, UCLA

URL: http://www.escholarship.org/uc/item/52v7g1sp

► We discuss an application of sparsity to *manifold* *learning*. We show that the activation patterns of an over-complete basis can be used to build a…
(more)

Subjects/Keywords: Statistics; manifold learning; simplices; sparsity

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APA (6^{th} Edition):

Flynn, J. J. (2014). Learning a simplicial structure using sparsity. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/52v7g1sp

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Flynn, John Joseph. “Learning a simplicial structure using sparsity.” 2014. Thesis, UCLA. Accessed February 22, 2020. http://www.escholarship.org/uc/item/52v7g1sp.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Flynn, John Joseph. “Learning a simplicial structure using sparsity.” 2014. Web. 22 Feb 2020.

Vancouver:

Flynn JJ. Learning a simplicial structure using sparsity. [Internet] [Thesis]. UCLA; 2014. [cited 2020 Feb 22]. Available from: http://www.escholarship.org/uc/item/52v7g1sp.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Flynn JJ. Learning a simplicial structure using sparsity. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/52v7g1sp

Not specified: Masters Thesis or Doctoral Dissertation

University of Newcastle

4.
Aziz, Md Fayeem Bin.
* Manifold* alignment through deep autoencoders.

Degree: PhD, 2019, University of Newcastle

URL: http://hdl.handle.net/1959.13/1407533

►

Research Doctorate - Doctor of Philosophy (PhD)

The focus of this thesis is on *manifold* alignment methods. They are applicable when aligning two or more…
(more)

Subjects/Keywords: autoencoder; manifold learning; manifold alignment; dimensionality reduction; convolutional autoencoder; deep learning

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APA (6^{th} Edition):

Aziz, M. F. B. (2019). Manifold alignment through deep autoencoders. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/1407533

Chicago Manual of Style (16^{th} Edition):

Aziz, Md Fayeem Bin. “Manifold alignment through deep autoencoders.” 2019. Doctoral Dissertation, University of Newcastle. Accessed February 22, 2020. http://hdl.handle.net/1959.13/1407533.

MLA Handbook (7^{th} Edition):

Aziz, Md Fayeem Bin. “Manifold alignment through deep autoencoders.” 2019. Web. 22 Feb 2020.

Vancouver:

Aziz MFB. Manifold alignment through deep autoencoders. [Internet] [Doctoral dissertation]. University of Newcastle; 2019. [cited 2020 Feb 22]. Available from: http://hdl.handle.net/1959.13/1407533.

Council of Science Editors:

Aziz MFB. Manifold alignment through deep autoencoders. [Doctoral Dissertation]. University of Newcastle; 2019. Available from: http://hdl.handle.net/1959.13/1407533

University of Newcastle

5. Paul, Rahul. Topological analysis, non-linear dimensionality reduction and optimisation applied to manifolds represented by point clouds.

Degree: PhD, 2018, University of Newcastle

URL: http://hdl.handle.net/1959.13/1393470

►

Research Doctorate - Doctor of Philosophy (PhD)

In recent years, there has been a growing demand for computational techniques that respect the non-linear structure of… (more)

Subjects/Keywords: manifold learning; point cloud; deep learning; optimisation

Record Details Similar Records

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APA (6^{th} Edition):

Paul, R. (2018). Topological analysis, non-linear dimensionality reduction and optimisation applied to manifolds represented by point clouds. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/1393470

Chicago Manual of Style (16^{th} Edition):

Paul, Rahul. “Topological analysis, non-linear dimensionality reduction and optimisation applied to manifolds represented by point clouds.” 2018. Doctoral Dissertation, University of Newcastle. Accessed February 22, 2020. http://hdl.handle.net/1959.13/1393470.

MLA Handbook (7^{th} Edition):

Paul, Rahul. “Topological analysis, non-linear dimensionality reduction and optimisation applied to manifolds represented by point clouds.” 2018. Web. 22 Feb 2020.

Vancouver:

Paul R. Topological analysis, non-linear dimensionality reduction and optimisation applied to manifolds represented by point clouds. [Internet] [Doctoral dissertation]. University of Newcastle; 2018. [cited 2020 Feb 22]. Available from: http://hdl.handle.net/1959.13/1393470.

Council of Science Editors:

Paul R. Topological analysis, non-linear dimensionality reduction and optimisation applied to manifolds represented by point clouds. [Doctoral Dissertation]. University of Newcastle; 2018. Available from: http://hdl.handle.net/1959.13/1393470

University of New South Wales

6. Kwok, Eric. Dynamic Isoperimetry on Graphs and Weighted Riemannian manifolds.

Degree: Mathematics & Statistics, 2018, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/59708 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49421/SOURCE02?view=true

► Transport and mixing in dynamical systems are important properties for many physical, chemical, biological, and engineering processes. The detection of transport barriers for dynamics with…
(more)

Subjects/Keywords: Lagrangian coherent structure; Dynamic; Isoperimetry; Weighted manifold; Graph; Manifold learning

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APA (6^{th} Edition):

Kwok, E. (2018). Dynamic Isoperimetry on Graphs and Weighted Riemannian manifolds. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/59708 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49421/SOURCE02?view=true

Chicago Manual of Style (16^{th} Edition):

Kwok, Eric. “Dynamic Isoperimetry on Graphs and Weighted Riemannian manifolds.” 2018. Doctoral Dissertation, University of New South Wales. Accessed February 22, 2020. http://handle.unsw.edu.au/1959.4/59708 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49421/SOURCE02?view=true.

MLA Handbook (7^{th} Edition):

Kwok, Eric. “Dynamic Isoperimetry on Graphs and Weighted Riemannian manifolds.” 2018. Web. 22 Feb 2020.

Vancouver:

Kwok E. Dynamic Isoperimetry on Graphs and Weighted Riemannian manifolds. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2020 Feb 22]. Available from: http://handle.unsw.edu.au/1959.4/59708 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49421/SOURCE02?view=true.

Council of Science Editors:

Kwok E. Dynamic Isoperimetry on Graphs and Weighted Riemannian manifolds. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/59708 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49421/SOURCE02?view=true

University of Utah

7.
Purcell, Michael Patrick.
Techniques in *manifold* *learning*: intrinsic dimension and principal surface estimation.

Degree: PhD, Mathematics;, 2010, University of Utah

URL: http://content.lib.utah.edu/cdm/singleitem/collection/etd2/id/1712/rec/1135

► Intrinsic dimension estimation is a fundamental problem in *manifold* *learning*. In applications, high-dimensional data frequently exhibit an underlying lower-dimensional structure that, if understood, would allow…
(more)

Subjects/Keywords: Intrinsic dimension; Principal surface; Manifold learning

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APA (6^{th} Edition):

Purcell, M. P. (2010). Techniques in manifold learning: intrinsic dimension and principal surface estimation. (Doctoral Dissertation). University of Utah. Retrieved from http://content.lib.utah.edu/cdm/singleitem/collection/etd2/id/1712/rec/1135

Chicago Manual of Style (16^{th} Edition):

Purcell, Michael Patrick. “Techniques in manifold learning: intrinsic dimension and principal surface estimation.” 2010. Doctoral Dissertation, University of Utah. Accessed February 22, 2020. http://content.lib.utah.edu/cdm/singleitem/collection/etd2/id/1712/rec/1135.

MLA Handbook (7^{th} Edition):

Purcell, Michael Patrick. “Techniques in manifold learning: intrinsic dimension and principal surface estimation.” 2010. Web. 22 Feb 2020.

Vancouver:

Purcell MP. Techniques in manifold learning: intrinsic dimension and principal surface estimation. [Internet] [Doctoral dissertation]. University of Utah; 2010. [cited 2020 Feb 22]. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd2/id/1712/rec/1135.

Council of Science Editors:

Purcell MP. Techniques in manifold learning: intrinsic dimension and principal surface estimation. [Doctoral Dissertation]. University of Utah; 2010. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd2/id/1712/rec/1135

University of Newcastle

8.
Wong, Aaron Seng Wai.
Implementing sensory perception and affect on humanoid robots using applications of *manifold* * learning*.

Degree: PhD, 2014, University of Newcastle

URL: http://hdl.handle.net/1959.13/1055370

►

Research Doctorate - Doctor of Philosophy (PhD)

Companion robots are popular entities in the world of science fiction; however, in the real world no robot… (more)

Subjects/Keywords: robots; manifold learning; affective computing; companion robots

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Wong, A. S. W. (2014). Implementing sensory perception and affect on humanoid robots using applications of manifold learning. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/1055370

Chicago Manual of Style (16^{th} Edition):

Wong, Aaron Seng Wai. “Implementing sensory perception and affect on humanoid robots using applications of manifold learning.” 2014. Doctoral Dissertation, University of Newcastle. Accessed February 22, 2020. http://hdl.handle.net/1959.13/1055370.

MLA Handbook (7^{th} Edition):

Wong, Aaron Seng Wai. “Implementing sensory perception and affect on humanoid robots using applications of manifold learning.” 2014. Web. 22 Feb 2020.

Vancouver:

Wong ASW. Implementing sensory perception and affect on humanoid robots using applications of manifold learning. [Internet] [Doctoral dissertation]. University of Newcastle; 2014. [cited 2020 Feb 22]. Available from: http://hdl.handle.net/1959.13/1055370.

Council of Science Editors:

Wong ASW. Implementing sensory perception and affect on humanoid robots using applications of manifold learning. [Doctoral Dissertation]. University of Newcastle; 2014. Available from: http://hdl.handle.net/1959.13/1055370

University of Southern California

9.
Deutsch, Shay.
* Learning* the geometric structure of high dimensional data
using the Tensor Voting Graph.

Degree: PhD, Computer Science, 2017, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3787

► This study addresses a range of fundamental problems in unsupervised *manifold* *learning*. Given a set of noisy points in a high dimensional space that lie…
(more)

Subjects/Keywords: manifold learning; unsupervised denoising; Tensor Voting Graph

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APA (6^{th} Edition):

Deutsch, S. (2017). Learning the geometric structure of high dimensional data using the Tensor Voting Graph. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3787

Chicago Manual of Style (16^{th} Edition):

Deutsch, Shay. “Learning the geometric structure of high dimensional data using the Tensor Voting Graph.” 2017. Doctoral Dissertation, University of Southern California. Accessed February 22, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3787.

MLA Handbook (7^{th} Edition):

Deutsch, Shay. “Learning the geometric structure of high dimensional data using the Tensor Voting Graph.” 2017. Web. 22 Feb 2020.

Vancouver:

Deutsch S. Learning the geometric structure of high dimensional data using the Tensor Voting Graph. [Internet] [Doctoral dissertation]. University of Southern California; 2017. [cited 2020 Feb 22]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3787.

Council of Science Editors:

Deutsch S. Learning the geometric structure of high dimensional data using the Tensor Voting Graph. [Doctoral Dissertation]. University of Southern California; 2017. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3787

University of Sydney

10.
De Deuge, Mark.
*Manifold**Learning* Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies
.

Degree: 2015, University of Sydney

URL: http://hdl.handle.net/2123/14551

► Field robots encounter dynamic unstructured environments containing a vast array of unique objects. In order to make sense of the world in which they are…
(more)

Subjects/Keywords: deep; learning; compressing; feature; hierarchy; manifold

Record Details Similar Records

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APA (6^{th} Edition):

De Deuge, M. (2015). Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/14551

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

De Deuge, Mark. “Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies .” 2015. Thesis, University of Sydney. Accessed February 22, 2020. http://hdl.handle.net/2123/14551.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

De Deuge, Mark. “Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies .” 2015. Web. 22 Feb 2020.

Vancouver:

De Deuge M. Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies . [Internet] [Thesis]. University of Sydney; 2015. [cited 2020 Feb 22]. Available from: http://hdl.handle.net/2123/14551.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

De Deuge M. Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies . [Thesis]. University of Sydney; 2015. Available from: http://hdl.handle.net/2123/14551

Not specified: Masters Thesis or Doctoral Dissertation

University of Technology, Sydney

11.
Zhou, T.
Compressed * learning*.

Degree: 2013, University of Technology, Sydney

URL: http://hdl.handle.net/10453/24180

► There has been an explosion of data derived from the internet and other digital sources. These data are usually multi-dimensional, massive in volume, frequently incomplete,…
(more)

Subjects/Keywords: Compressed learning.; Sparse learning.; Machine learning.; Manifold learning.; Big data

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Zhou, T. (2013). Compressed learning. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/24180

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Zhou, T. “Compressed learning.” 2013. Thesis, University of Technology, Sydney. Accessed February 22, 2020. http://hdl.handle.net/10453/24180.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhou, T. “Compressed learning.” 2013. Web. 22 Feb 2020.

Vancouver:

Zhou T. Compressed learning. [Internet] [Thesis]. University of Technology, Sydney; 2013. [cited 2020 Feb 22]. Available from: http://hdl.handle.net/10453/24180.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhou T. Compressed learning. [Thesis]. University of Technology, Sydney; 2013. Available from: http://hdl.handle.net/10453/24180

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Merced

12.
Vladymyrov, Maksym.
Large-Scale Methods for Nonlinear *Manifold* * Learning*.

Degree: Electrical Engineering and Computer Science, 2014, University of California – Merced

URL: http://www.escholarship.org/uc/item/9hj5v8z2

► High-dimensional data representation is an important problem in many different areas of science. Nowadays, it is becoming crucial to interpret the data of varying dimensionality…
(more)

Subjects/Keywords: Computer science; dimensionality reduction; machine learning; manifold learning; unsupervised learning

Record Details Similar Records

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APA (6^{th} Edition):

Vladymyrov, M. (2014). Large-Scale Methods for Nonlinear Manifold Learning. (Thesis). University of California – Merced. Retrieved from http://www.escholarship.org/uc/item/9hj5v8z2

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Vladymyrov, Maksym. “Large-Scale Methods for Nonlinear Manifold Learning.” 2014. Thesis, University of California – Merced. Accessed February 22, 2020. http://www.escholarship.org/uc/item/9hj5v8z2.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Vladymyrov, Maksym. “Large-Scale Methods for Nonlinear Manifold Learning.” 2014. Web. 22 Feb 2020.

Vancouver:

Vladymyrov M. Large-Scale Methods for Nonlinear Manifold Learning. [Internet] [Thesis]. University of California – Merced; 2014. [cited 2020 Feb 22]. Available from: http://www.escholarship.org/uc/item/9hj5v8z2.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vladymyrov M. Large-Scale Methods for Nonlinear Manifold Learning. [Thesis]. University of California – Merced; 2014. Available from: http://www.escholarship.org/uc/item/9hj5v8z2

Not specified: Masters Thesis or Doctoral Dissertation

Northeastern University

13.
Shaker, Matineh.
*Manifold**learning* and unwrapping using density ridges.

Degree: PhD, Department of Electrical and Computer Engineering, 2016, Northeastern University

URL: http://hdl.handle.net/2047/D20260369

► *Manifold* *learning* is used for determining a coordinate system for high dimensional data on its intrinsic low-dimensional *manifold*, in order to (approximately) unwrap the *manifold*…
(more)

Subjects/Keywords: dimensionality reduction; machine learning; manifold learning; sparse learning; statistical modeling

Record Details Similar Records

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APA (6^{th} Edition):

Shaker, M. (2016). Manifold learning and unwrapping using density ridges. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20260369

Chicago Manual of Style (16^{th} Edition):

Shaker, Matineh. “Manifold learning and unwrapping using density ridges.” 2016. Doctoral Dissertation, Northeastern University. Accessed February 22, 2020. http://hdl.handle.net/2047/D20260369.

MLA Handbook (7^{th} Edition):

Shaker, Matineh. “Manifold learning and unwrapping using density ridges.” 2016. Web. 22 Feb 2020.

Vancouver:

Shaker M. Manifold learning and unwrapping using density ridges. [Internet] [Doctoral dissertation]. Northeastern University; 2016. [cited 2020 Feb 22]. Available from: http://hdl.handle.net/2047/D20260369.

Council of Science Editors:

Shaker M. Manifold learning and unwrapping using density ridges. [Doctoral Dissertation]. Northeastern University; 2016. Available from: http://hdl.handle.net/2047/D20260369

14.
Tran, Loc.
High Dimensional Data Set Analysis Using a Large-Scale *Manifold* *Learning* Approach.

Degree: PhD, Electrical/Computer Engineering, 2014, Old Dominion University

URL: 9781321316513 ; https://digitalcommons.odu.edu/ece_etds/186

► Because of technological advances, a trend occurs for data sets increasing in size and dimensionality. Processing these large scale data sets is challenging for…
(more)

Subjects/Keywords: Manifold learning; Sparse learning; Manifolds; Big data; Computer Engineering; Computer Sciences

Record Details Similar Records

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APA (6^{th} Edition):

Tran, L. (2014). High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach. (Doctoral Dissertation). Old Dominion University. Retrieved from 9781321316513 ; https://digitalcommons.odu.edu/ece_etds/186

Chicago Manual of Style (16^{th} Edition):

Tran, Loc. “High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach.” 2014. Doctoral Dissertation, Old Dominion University. Accessed February 22, 2020. 9781321316513 ; https://digitalcommons.odu.edu/ece_etds/186.

MLA Handbook (7^{th} Edition):

Tran, Loc. “High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach.” 2014. Web. 22 Feb 2020.

Vancouver:

Tran L. High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach. [Internet] [Doctoral dissertation]. Old Dominion University; 2014. [cited 2020 Feb 22]. Available from: 9781321316513 ; https://digitalcommons.odu.edu/ece_etds/186.

Council of Science Editors:

Tran L. High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach. [Doctoral Dissertation]. Old Dominion University; 2014. Available from: 9781321316513 ; https://digitalcommons.odu.edu/ece_etds/186

University of Washington

15.
Mohammed, Kitty.
Statistical Methods for *Manifold* Recovery and C^{1, 1} Regression on Manifolds.

Degree: PhD, 2019, University of Washington

URL: http://hdl.handle.net/1773/43748

► High-dimensional data sets often have lower-dimensional structure taking the form of a submanifold of a Euclidean space. It is challenging but necessary to develop statistical…
(more)

Subjects/Keywords: machine learning; manifold learning; nonparametric regression; statistics; Statistics; Statistics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Mohammed, K. (2019). Statistical Methods for Manifold Recovery and C^{1, 1} Regression on Manifolds. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/43748

Chicago Manual of Style (16^{th} Edition):

Mohammed, Kitty. “Statistical Methods for Manifold Recovery and C^{1, 1} Regression on Manifolds.” 2019. Doctoral Dissertation, University of Washington. Accessed February 22, 2020. http://hdl.handle.net/1773/43748.

MLA Handbook (7^{th} Edition):

Mohammed, Kitty. “Statistical Methods for Manifold Recovery and C^{1, 1} Regression on Manifolds.” 2019. Web. 22 Feb 2020.

Vancouver:

Mohammed K. Statistical Methods for Manifold Recovery and C^{1, 1} Regression on Manifolds. [Internet] [Doctoral dissertation]. University of Washington; 2019. [cited 2020 Feb 22]. Available from: http://hdl.handle.net/1773/43748.

Council of Science Editors:

Mohammed K. Statistical Methods for Manifold Recovery and C^{1, 1} Regression on Manifolds. [Doctoral Dissertation]. University of Washington; 2019. Available from: http://hdl.handle.net/1773/43748

University of Washington

16.
McQueen, James.
Scalable *Manifold* *Learning* and Related Topics.

Degree: PhD, 2017, University of Washington

URL: http://hdl.handle.net/1773/40305

► The *subject* of *manifold* *learning* is vast and still largely unexplored. As a subset of unsupervised *learning* it has a fundamental challenge in adequately defining…
(more)

Subjects/Keywords: Clustering; Machine Learning; Manifold Learning; Non-Linear Dimension Reduction; Unsupervised Learning; Statistics; Statistics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

McQueen, J. (2017). Scalable Manifold Learning and Related Topics. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/40305

Chicago Manual of Style (16^{th} Edition):

McQueen, James. “Scalable Manifold Learning and Related Topics.” 2017. Doctoral Dissertation, University of Washington. Accessed February 22, 2020. http://hdl.handle.net/1773/40305.

MLA Handbook (7^{th} Edition):

McQueen, James. “Scalable Manifold Learning and Related Topics.” 2017. Web. 22 Feb 2020.

Vancouver:

McQueen J. Scalable Manifold Learning and Related Topics. [Internet] [Doctoral dissertation]. University of Washington; 2017. [cited 2020 Feb 22]. Available from: http://hdl.handle.net/1773/40305.

Council of Science Editors:

McQueen J. Scalable Manifold Learning and Related Topics. [Doctoral Dissertation]. University of Washington; 2017. Available from: http://hdl.handle.net/1773/40305

University of Alberta

17. Rayner, David Christopher Ferguson. Optimization for Heuristic Search.

Degree: PhD, Department of Computing Science, 2014, University of Alberta

URL: https://era.library.ualberta.ca/files/st74cs84d

► Heuristic search is a central problem in artificial intelligence. Among its defining properties is the use of a heuristic, a scalar function mapping pairs of…
(more)

Subjects/Keywords: search; graph embedding; manifold learning; pathfinding; optimization; heuristics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Rayner, D. C. F. (2014). Optimization for Heuristic Search. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/st74cs84d

Chicago Manual of Style (16^{th} Edition):

Rayner, David Christopher Ferguson. “Optimization for Heuristic Search.” 2014. Doctoral Dissertation, University of Alberta. Accessed February 22, 2020. https://era.library.ualberta.ca/files/st74cs84d.

MLA Handbook (7^{th} Edition):

Rayner, David Christopher Ferguson. “Optimization for Heuristic Search.” 2014. Web. 22 Feb 2020.

Vancouver:

Rayner DCF. Optimization for Heuristic Search. [Internet] [Doctoral dissertation]. University of Alberta; 2014. [cited 2020 Feb 22]. Available from: https://era.library.ualberta.ca/files/st74cs84d.

Council of Science Editors:

Rayner DCF. Optimization for Heuristic Search. [Doctoral Dissertation]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/st74cs84d

UCLA

18. Zhu, Wei. Nonlocal Variational Methods in Image and Data Processing.

Degree: Mathematics, 2017, UCLA

URL: http://www.escholarship.org/uc/item/1b16b6q7

► In this dissertation, two nonlocal variational models for image and data processing are presented: nonlocal total variation (NLTV) for unsupervised hyperspectral image classification, and low…
(more)

Subjects/Keywords: Applied mathematics; hyperspectral images; image processing; manifold learning; nonlocal variational methods

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APA (6^{th} Edition):

Zhu, W. (2017). Nonlocal Variational Methods in Image and Data Processing. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/1b16b6q7

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Zhu, Wei. “Nonlocal Variational Methods in Image and Data Processing.” 2017. Thesis, UCLA. Accessed February 22, 2020. http://www.escholarship.org/uc/item/1b16b6q7.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhu, Wei. “Nonlocal Variational Methods in Image and Data Processing.” 2017. Web. 22 Feb 2020.

Vancouver:

Zhu W. Nonlocal Variational Methods in Image and Data Processing. [Internet] [Thesis]. UCLA; 2017. [cited 2020 Feb 22]. Available from: http://www.escholarship.org/uc/item/1b16b6q7.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhu W. Nonlocal Variational Methods in Image and Data Processing. [Thesis]. UCLA; 2017. Available from: http://www.escholarship.org/uc/item/1b16b6q7

Not specified: Masters Thesis or Doctoral Dissertation

Oklahoma State University

19.
Venkataraman, Vijay.
Advanced Machine *Learning* Approaches for Target Detection, Tracking and Recognition.

Degree: School of Electrical & Computer Engineering, 2010, Oklahoma State University

URL: http://hdl.handle.net/11244/7875

► This dissertation addresses the key technical components of an Automatic Target Recognition (ATR) system namely: target detection, tracking, *learning* and recognition. Novel solutions are proposed…
(more)

Subjects/Keywords: appearance learning; atr; flir; identity manifold; particle filter; tensor decomposition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Venkataraman, V. (2010). Advanced Machine Learning Approaches for Target Detection, Tracking and Recognition. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/7875

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Venkataraman, Vijay. “Advanced Machine Learning Approaches for Target Detection, Tracking and Recognition.” 2010. Thesis, Oklahoma State University. Accessed February 22, 2020. http://hdl.handle.net/11244/7875.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Venkataraman, Vijay. “Advanced Machine Learning Approaches for Target Detection, Tracking and Recognition.” 2010. Web. 22 Feb 2020.

Vancouver:

Venkataraman V. Advanced Machine Learning Approaches for Target Detection, Tracking and Recognition. [Internet] [Thesis]. Oklahoma State University; 2010. [cited 2020 Feb 22]. Available from: http://hdl.handle.net/11244/7875.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Venkataraman V. Advanced Machine Learning Approaches for Target Detection, Tracking and Recognition. [Thesis]. Oklahoma State University; 2010. Available from: http://hdl.handle.net/11244/7875

Not specified: Masters Thesis or Doctoral Dissertation

Princeton University

20.
Holiday, Alexander.
*Manifold**learning* for coarse-graining networks and for parameter reduction
.

Degree: PhD, 2017, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01j098zd77v

► Recent decades have seen a tremendous rise in the affordability and performance of various computational technologies, enabling researchers to propose and probe ever more complicated…
(more)

Subjects/Keywords: diffusion maps; dimensionality reduction; manifold learning; networks; parameter reduction

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Holiday, A. (2017). Manifold learning for coarse-graining networks and for parameter reduction . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01j098zd77v

Chicago Manual of Style (16^{th} Edition):

Holiday, Alexander. “Manifold learning for coarse-graining networks and for parameter reduction .” 2017. Doctoral Dissertation, Princeton University. Accessed February 22, 2020. http://arks.princeton.edu/ark:/88435/dsp01j098zd77v.

MLA Handbook (7^{th} Edition):

Holiday, Alexander. “Manifold learning for coarse-graining networks and for parameter reduction .” 2017. Web. 22 Feb 2020.

Vancouver:

Holiday A. Manifold learning for coarse-graining networks and for parameter reduction . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2020 Feb 22]. Available from: http://arks.princeton.edu/ark:/88435/dsp01j098zd77v.

Council of Science Editors:

Holiday A. Manifold learning for coarse-graining networks and for parameter reduction . [Doctoral Dissertation]. Princeton University; 2017. Available from: http://arks.princeton.edu/ark:/88435/dsp01j098zd77v

Princeton University

21. Pozharskiy, Dmitry. Two Studies of Complex Nonlinear Systems: Engineered Granular Crystals and Coarse-Graining Optimization Problems .

Degree: PhD, 2018, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp010v838323b

► In recent years a nonlinear, acoustic metamaterial, named granular crystals, has gained prominence due to its high accessibility, both experimentally and compu- tationally. The observation…
(more)

Subjects/Keywords: bifurcation analysis; granular crystals; manifold learning; nonlinear dynamics; optimization

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APA (6^{th} Edition):

Pozharskiy, D. (2018). Two Studies of Complex Nonlinear Systems: Engineered Granular Crystals and Coarse-Graining Optimization Problems . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp010v838323b

Chicago Manual of Style (16^{th} Edition):

Pozharskiy, Dmitry. “Two Studies of Complex Nonlinear Systems: Engineered Granular Crystals and Coarse-Graining Optimization Problems .” 2018. Doctoral Dissertation, Princeton University. Accessed February 22, 2020. http://arks.princeton.edu/ark:/88435/dsp010v838323b.

MLA Handbook (7^{th} Edition):

Pozharskiy, Dmitry. “Two Studies of Complex Nonlinear Systems: Engineered Granular Crystals and Coarse-Graining Optimization Problems .” 2018. Web. 22 Feb 2020.

Vancouver:

Pozharskiy D. Two Studies of Complex Nonlinear Systems: Engineered Granular Crystals and Coarse-Graining Optimization Problems . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2020 Feb 22]. Available from: http://arks.princeton.edu/ark:/88435/dsp010v838323b.

Council of Science Editors:

Pozharskiy D. Two Studies of Complex Nonlinear Systems: Engineered Granular Crystals and Coarse-Graining Optimization Problems . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp010v838323b

University of California – San Diego

22.
Babaeian, Amir.
Part I - Constrained Shortest-Path For *Manifold* *Learning* And Multiple *Manifold* Clustering Part II - Community Detection In Large Graphs; Analysis, Design And Implementation.

Degree: Mathematics, 2017, University of California – San Diego

URL: http://www.escholarship.org/uc/item/4w64w79x

► In Part I of this thesis, we address the problem of *manifold* *learning* and clustering by introducing a novel constrained shortest-path algorithm. In the case…
(more)

Subjects/Keywords: Mathematics; Statistics; Computer science; Community Detection; Constrained-Shortest-Path; Large Graphs; Manifold Learning; MapReduce; Multiple Manifold Clustering

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Babaeian, A. (2017). Part I - Constrained Shortest-Path For Manifold Learning And Multiple Manifold Clustering Part II - Community Detection In Large Graphs; Analysis, Design And Implementation. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/4w64w79x

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Babaeian, Amir. “Part I - Constrained Shortest-Path For Manifold Learning And Multiple Manifold Clustering Part II - Community Detection In Large Graphs; Analysis, Design And Implementation.” 2017. Thesis, University of California – San Diego. Accessed February 22, 2020. http://www.escholarship.org/uc/item/4w64w79x.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Babaeian, Amir. “Part I - Constrained Shortest-Path For Manifold Learning And Multiple Manifold Clustering Part II - Community Detection In Large Graphs; Analysis, Design And Implementation.” 2017. Web. 22 Feb 2020.

Vancouver:

Babaeian A. Part I - Constrained Shortest-Path For Manifold Learning And Multiple Manifold Clustering Part II - Community Detection In Large Graphs; Analysis, Design And Implementation. [Internet] [Thesis]. University of California – San Diego; 2017. [cited 2020 Feb 22]. Available from: http://www.escholarship.org/uc/item/4w64w79x.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Babaeian A. Part I - Constrained Shortest-Path For Manifold Learning And Multiple Manifold Clustering Part II - Community Detection In Large Graphs; Analysis, Design And Implementation. [Thesis]. University of California – San Diego; 2017. Available from: http://www.escholarship.org/uc/item/4w64w79x

Not specified: Masters Thesis or Doctoral Dissertation

University of Cincinnati

23. Fang, Chunsheng. Novel Frameworks for Mining Heterogeneous and Dynamic Networks.

Degree: PhD, Engineering and Applied Science: Computer Science and Engineering, 2011, University of Cincinnati

URL: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978

► Graphs serve as an important tool for discrete data representation. Recently, graph representations have made possible very powerful machine *learning* algorithms, such as *manifold* *learning*,…
(more)

Subjects/Keywords: Computer Science; machine learning; social network; data mining; manifold learning; graph embedding; dynamic graph

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Fang, C. (2011). Novel Frameworks for Mining Heterogeneous and Dynamic Networks. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978

Chicago Manual of Style (16^{th} Edition):

Fang, Chunsheng. “Novel Frameworks for Mining Heterogeneous and Dynamic Networks.” 2011. Doctoral Dissertation, University of Cincinnati. Accessed February 22, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978.

MLA Handbook (7^{th} Edition):

Fang, Chunsheng. “Novel Frameworks for Mining Heterogeneous and Dynamic Networks.” 2011. Web. 22 Feb 2020.

Vancouver:

Fang C. Novel Frameworks for Mining Heterogeneous and Dynamic Networks. [Internet] [Doctoral dissertation]. University of Cincinnati; 2011. [cited 2020 Feb 22]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978.

Council of Science Editors:

Fang C. Novel Frameworks for Mining Heterogeneous and Dynamic Networks. [Doctoral Dissertation]. University of Cincinnati; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978

University of Colorado

24.
Ramirez Jr., Juan.
* Learning* from

Degree: MS, Electrical, Computer & Energy Engineering, 2012, University of Colorado

URL: https://scholar.colorado.edu/ecen_gradetds/44

► Over the past several years, advances in sensor technology has lead to increases in the demand for computerized methods for analyzing seismological signals. Central…
(more)

Subjects/Keywords: Machine Learning; Manifold-Valued Data; Seismology; Supervised Learning; Electrical and Computer Engineering

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APA (6^{th} Edition):

Ramirez Jr., J. (2012). Learning from Manifold-Valued Data: An Application to Seismic Signal Processing. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/ecen_gradetds/44

Chicago Manual of Style (16^{th} Edition):

Ramirez Jr., Juan. “Learning from Manifold-Valued Data: An Application to Seismic Signal Processing.” 2012. Masters Thesis, University of Colorado. Accessed February 22, 2020. https://scholar.colorado.edu/ecen_gradetds/44.

MLA Handbook (7^{th} Edition):

Ramirez Jr., Juan. “Learning from Manifold-Valued Data: An Application to Seismic Signal Processing.” 2012. Web. 22 Feb 2020.

Vancouver:

Ramirez Jr. J. Learning from Manifold-Valued Data: An Application to Seismic Signal Processing. [Internet] [Masters thesis]. University of Colorado; 2012. [cited 2020 Feb 22]. Available from: https://scholar.colorado.edu/ecen_gradetds/44.

Council of Science Editors:

Ramirez Jr. J. Learning from Manifold-Valued Data: An Application to Seismic Signal Processing. [Masters Thesis]. University of Colorado; 2012. Available from: https://scholar.colorado.edu/ecen_gradetds/44

25. ZHANG SHENG. Exploring face space: A computational approach.

Degree: 2007, National University of Singapore

URL: http://scholarbank.nus.edu.sg/handle/10635/13430

Subjects/Keywords: face space; statistical learning; manifold learning

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

SHENG, Z. (2007). Exploring face space: A computational approach. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/13430

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

SHENG, ZHANG. “Exploring face space: A computational approach.” 2007. Thesis, National University of Singapore. Accessed February 22, 2020. http://scholarbank.nus.edu.sg/handle/10635/13430.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

SHENG, ZHANG. “Exploring face space: A computational approach.” 2007. Web. 22 Feb 2020.

Vancouver:

SHENG Z. Exploring face space: A computational approach. [Internet] [Thesis]. National University of Singapore; 2007. [cited 2020 Feb 22]. Available from: http://scholarbank.nus.edu.sg/handle/10635/13430.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

SHENG Z. Exploring face space: A computational approach. [Thesis]. National University of Singapore; 2007. Available from: http://scholarbank.nus.edu.sg/handle/10635/13430

Not specified: Masters Thesis or Doctoral Dissertation

26.
Wang, Chang.
A Geometric Framework for Transfer *Learning* Using *Manifold* Alignment.

Degree: PhD, Computer Science, 2010, U of Massachusetts : PhD

URL: https://scholarworks.umass.edu/open_access_dissertations/269

► Many machine *learning* problems involve dealing with a large amount of high-dimensional data across diverse domains. In addition, annotating or labeling the data is expensive…
(more)

Subjects/Keywords: Dimensionality Reduction; Manifold Alignment; Multiscale Analysis; Representation Learning; Topic Model; Transfer Learning; Computer Sciences

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Wang, C. (2010). A Geometric Framework for Transfer Learning Using Manifold Alignment. (Doctoral Dissertation). U of Massachusetts : PhD. Retrieved from https://scholarworks.umass.edu/open_access_dissertations/269

Chicago Manual of Style (16^{th} Edition):

Wang, Chang. “A Geometric Framework for Transfer Learning Using Manifold Alignment.” 2010. Doctoral Dissertation, U of Massachusetts : PhD. Accessed February 22, 2020. https://scholarworks.umass.edu/open_access_dissertations/269.

MLA Handbook (7^{th} Edition):

Wang, Chang. “A Geometric Framework for Transfer Learning Using Manifold Alignment.” 2010. Web. 22 Feb 2020.

Vancouver:

Wang C. A Geometric Framework for Transfer Learning Using Manifold Alignment. [Internet] [Doctoral dissertation]. U of Massachusetts : PhD; 2010. [cited 2020 Feb 22]. Available from: https://scholarworks.umass.edu/open_access_dissertations/269.

Council of Science Editors:

Wang C. A Geometric Framework for Transfer Learning Using Manifold Alignment. [Doctoral Dissertation]. U of Massachusetts : PhD; 2010. Available from: https://scholarworks.umass.edu/open_access_dissertations/269

Rochester Institute of Technology

27.
Minnehan, Breton Lawrence.
Deep Grassmann *Manifold* Optimization for Computer Vision.

Degree: PhD, Engineering, 2019, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/10122

► In this work, we propose methods that advance four areas in the field of computer vision: dimensionality reduction, deep feature embeddings, visual domain adaptation,…
(more)

Subjects/Keywords: Computer vision; Deep learning; Domain adaption; Feature learning; Manifold optimization; Network compression

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Minnehan, B. L. (2019). Deep Grassmann Manifold Optimization for Computer Vision. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10122

Chicago Manual of Style (16^{th} Edition):

Minnehan, Breton Lawrence. “Deep Grassmann Manifold Optimization for Computer Vision.” 2019. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 22, 2020. https://scholarworks.rit.edu/theses/10122.

MLA Handbook (7^{th} Edition):

Minnehan, Breton Lawrence. “Deep Grassmann Manifold Optimization for Computer Vision.” 2019. Web. 22 Feb 2020.

Vancouver:

Minnehan BL. Deep Grassmann Manifold Optimization for Computer Vision. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2019. [cited 2020 Feb 22]. Available from: https://scholarworks.rit.edu/theses/10122.

Council of Science Editors:

Minnehan BL. Deep Grassmann Manifold Optimization for Computer Vision. [Doctoral Dissertation]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10122

Royal Holloway, University of London

28. Kou, Jiaxin. Faithful visualisation of similarities in high dimensional data.

Degree: PhD, 2016, Royal Holloway, University of London

URL: https://pure.royalholloway.ac.uk/portal/en/publications/faithful-visualisation-of-similarities-in-high-dimensional-data(675d46d9-bc6d-4c1c-ab69-bc6c56153497).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.792581

► In the last fifteen years, new methods of dimension reduction have been invented that enable much improved visualisation of high-dimensional data-sets. Conventionally, the data-sets are…
(more)

Subjects/Keywords: High Dimensional Data; Visualisation; Graph Theory; Machine Learning; Manifold Learning; Dimensionality Reduction; Overlay Graph

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Kou, J. (2016). Faithful visualisation of similarities in high dimensional data. (Doctoral Dissertation). Royal Holloway, University of London. Retrieved from https://pure.royalholloway.ac.uk/portal/en/publications/faithful-visualisation-of-similarities-in-high-dimensional-data(675d46d9-bc6d-4c1c-ab69-bc6c56153497).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.792581

Chicago Manual of Style (16^{th} Edition):

Kou, Jiaxin. “Faithful visualisation of similarities in high dimensional data.” 2016. Doctoral Dissertation, Royal Holloway, University of London. Accessed February 22, 2020. https://pure.royalholloway.ac.uk/portal/en/publications/faithful-visualisation-of-similarities-in-high-dimensional-data(675d46d9-bc6d-4c1c-ab69-bc6c56153497).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.792581.

MLA Handbook (7^{th} Edition):

Kou, Jiaxin. “Faithful visualisation of similarities in high dimensional data.” 2016. Web. 22 Feb 2020.

Vancouver:

Kou J. Faithful visualisation of similarities in high dimensional data. [Internet] [Doctoral dissertation]. Royal Holloway, University of London; 2016. [cited 2020 Feb 22]. Available from: https://pure.royalholloway.ac.uk/portal/en/publications/faithful-visualisation-of-similarities-in-high-dimensional-data(675d46d9-bc6d-4c1c-ab69-bc6c56153497).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.792581.

Council of Science Editors:

Kou J. Faithful visualisation of similarities in high dimensional data. [Doctoral Dissertation]. Royal Holloway, University of London; 2016. Available from: https://pure.royalholloway.ac.uk/portal/en/publications/faithful-visualisation-of-similarities-in-high-dimensional-data(675d46d9-bc6d-4c1c-ab69-bc6c56153497).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.792581

29. Wang, Hao. Objects Extraction and Recognition for Camera-Based Interaction: Heuristic and Statistical Approaches.

Degree: 2008, Helsinki University of Technology

URL: http://lib.tkk.fi/Diss/2007/isbn9789512291342/

►

In this thesis, heuristic and probabilistic methods are applied to a number of problems for camera-based interactions. The goal is to provide solutions for a… (more)

Subjects/Keywords: camera-based interaction; text extraction; bar code; facial expression; boosting; manifold learning

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Wang, H. (2008). Objects Extraction and Recognition for Camera-Based Interaction: Heuristic and Statistical Approaches. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/2007/isbn9789512291342/

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Wang, Hao. “Objects Extraction and Recognition for Camera-Based Interaction: Heuristic and Statistical Approaches.” 2008. Thesis, Helsinki University of Technology. Accessed February 22, 2020. http://lib.tkk.fi/Diss/2007/isbn9789512291342/.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Wang, Hao. “Objects Extraction and Recognition for Camera-Based Interaction: Heuristic and Statistical Approaches.” 2008. Web. 22 Feb 2020.

Vancouver:

Wang H. Objects Extraction and Recognition for Camera-Based Interaction: Heuristic and Statistical Approaches. [Internet] [Thesis]. Helsinki University of Technology; 2008. [cited 2020 Feb 22]. Available from: http://lib.tkk.fi/Diss/2007/isbn9789512291342/.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang H. Objects Extraction and Recognition for Camera-Based Interaction: Heuristic and Statistical Approaches. [Thesis]. Helsinki University of Technology; 2008. Available from: http://lib.tkk.fi/Diss/2007/isbn9789512291342/

Not specified: Masters Thesis or Doctoral Dissertation

30. Tang, Xiaoying. BRAIN SEGMENTATION VIA DIFFEOMORPHIC LIKELIHOOD FUSION AND ITS APPLICATIONS TO CLINICAL ANALYSES.

Degree: 2014, Johns Hopkins University

URL: http://jhir.library.jhu.edu/handle/1774.2/37928

► The human brain is composed of a variety of structures, or regions of interest (ROIs), that are responsible for a range of functions. It is…
(more)

Subjects/Keywords: Brain Segmentation; Diffeomorphic Likelihood Fusion; Statistical Shape Analysis; Image Registration; Manifold Learning and Clustering; Neuroinformatics

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Tang, X. (2014). BRAIN SEGMENTATION VIA DIFFEOMORPHIC LIKELIHOOD FUSION AND ITS APPLICATIONS TO CLINICAL ANALYSES. (Thesis). Johns Hopkins University. Retrieved from http://jhir.library.jhu.edu/handle/1774.2/37928

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Tang, Xiaoying. “BRAIN SEGMENTATION VIA DIFFEOMORPHIC LIKELIHOOD FUSION AND ITS APPLICATIONS TO CLINICAL ANALYSES.” 2014. Thesis, Johns Hopkins University. Accessed February 22, 2020. http://jhir.library.jhu.edu/handle/1774.2/37928.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Tang, Xiaoying. “BRAIN SEGMENTATION VIA DIFFEOMORPHIC LIKELIHOOD FUSION AND ITS APPLICATIONS TO CLINICAL ANALYSES.” 2014. Web. 22 Feb 2020.

Vancouver:

Tang X. BRAIN SEGMENTATION VIA DIFFEOMORPHIC LIKELIHOOD FUSION AND ITS APPLICATIONS TO CLINICAL ANALYSES. [Internet] [Thesis]. Johns Hopkins University; 2014. [cited 2020 Feb 22]. Available from: http://jhir.library.jhu.edu/handle/1774.2/37928.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tang X. BRAIN SEGMENTATION VIA DIFFEOMORPHIC LIKELIHOOD FUSION AND ITS APPLICATIONS TO CLINICAL ANALYSES. [Thesis]. Johns Hopkins University; 2014. Available from: http://jhir.library.jhu.edu/handle/1774.2/37928

Not specified: Masters Thesis or Doctoral Dissertation