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108 total matches.

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- 2010 – 2014 (48)
- 2005 – 2009 (19)

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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)

Record Details Similar Records

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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 October 22, 2019. 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 Oct 2019.

Vancouver:

Ahmed, Talal 1. Geometric manifold approximation using locally linear approximations. [Internet] [Masters thesis]. Rutgers University; 2016. [cited 2019 Oct 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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 October 22, 2019. 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 Oct 2019.

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 2019 Oct 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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 October 22, 2019. 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 Oct 2019.

Vancouver:

Flynn JJ. Learning a simplicial structure using sparsity. [Internet] [Thesis]. UCLA; 2014. [cited 2019 Oct 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. 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

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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 October 22, 2019. 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 Oct 2019.

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 2019 Oct 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

5. 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 October 22, 2019. 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 Oct 2019.

Vancouver:

Kwok E. Dynamic Isoperimetry on Graphs and Weighted Riemannian manifolds. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2019 Oct 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

6.
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 October 22, 2019. 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 Oct 2019.

Vancouver:

Purcell MP. Techniques in manifold learning: intrinsic dimension and principal surface estimation. [Internet] [Doctoral dissertation]. University of Utah; 2010. [cited 2019 Oct 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 Southern California

7.
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

Record Details Similar Records

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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 October 22, 2019. 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 Oct 2019.

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 2019 Oct 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 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 October 22, 2019. 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 Oct 2019.

Vancouver:

Wong ASW. Implementing sensory perception and affect on humanoid robots using applications of manifold learning. [Internet] [Doctoral dissertation]. University of Newcastle; 2014. [cited 2019 Oct 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 Sydney

9.
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 October 22, 2019. 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 Oct 2019.

Vancouver:

De Deuge M. Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies . [Internet] [Thesis]. University of Sydney; 2015. [cited 2019 Oct 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

10.
Zhou, Tianyi.
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 (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, Tianyi. “Compressed learning.” 2013. Thesis, University of Technology, Sydney. Accessed October 22, 2019. http://hdl.handle.net/10453/24180.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhou, Tianyi. “Compressed learning.” 2013. Web. 22 Oct 2019.

Vancouver:

Zhou T. Compressed learning. [Internet] [Thesis]. University of Technology, Sydney; 2013. [cited 2019 Oct 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

11.
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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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 October 22, 2019. 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 Oct 2019.

Vancouver:

Vladymyrov M. Large-Scale Methods for Nonlinear Manifold Learning. [Internet] [Thesis]. University of California – Merced; 2014. [cited 2019 Oct 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

12.
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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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 October 22, 2019. http://hdl.handle.net/2047/D20260369.

MLA Handbook (7^{th} Edition):

Shaker, Matineh. “Manifold learning and unwrapping using density ridges.” 2016. Web. 22 Oct 2019.

Vancouver:

Shaker M. Manifold learning and unwrapping using density ridges. [Internet] [Doctoral dissertation]. Northeastern University; 2016. [cited 2019 Oct 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

University of Washington

13.
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

Record Details Similar Records

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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 October 22, 2019. 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 Oct 2019.

Vancouver:

Mohammed K. Statistical Methods for Manifold Recovery and C^{1, 1} Regression on Manifolds. [Internet] [Doctoral dissertation]. University of Washington; 2019. [cited 2019 Oct 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

EPFL

14. de Morsier, Frank. Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images.

Degree: 2014, EPFL

URL: http://infoscience.epfl.ch/record/199126

► The main challenge of new information technologies is to retrieve intelligible information from the large volume of digital data gathered every day. Among the variety…
(more)

Subjects/Keywords: Machine learning; Kernel methods; Change detection; Clustering; Manifold learning; Pattern recognition; Semi-supervised learning

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

APA (6^{th} Edition):

de Morsier, F. (2014). Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/199126

Not specified: Masters Thesis or Doctoral Dissertation

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

de Morsier, Frank. “Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images.” 2014. Thesis, EPFL. Accessed October 22, 2019. http://infoscience.epfl.ch/record/199126.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

de Morsier, Frank. “Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images.” 2014. Web. 22 Oct 2019.

Vancouver:

de Morsier F. Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images. [Internet] [Thesis]. EPFL; 2014. [cited 2019 Oct 22]. Available from: http://infoscience.epfl.ch/record/199126.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

de Morsier F. Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images. [Thesis]. EPFL; 2014. Available from: http://infoscience.epfl.ch/record/199126

Not specified: Masters Thesis or Doctoral Dissertation

University of Washington

15.
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 (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 October 22, 2019. http://hdl.handle.net/1773/40305.

MLA Handbook (7^{th} Edition):

McQueen, James. “Scalable Manifold Learning and Related Topics.” 2017. Web. 22 Oct 2019.

Vancouver:

McQueen J. Scalable Manifold Learning and Related Topics. [Internet] [Doctoral dissertation]. University of Washington; 2017. [cited 2019 Oct 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

16. 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 (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 October 22, 2019. https://era.library.ualberta.ca/files/st74cs84d.

MLA Handbook (7^{th} Edition):

Rayner, David Christopher Ferguson. “Optimization for Heuristic Search.” 2014. Web. 22 Oct 2019.

Vancouver:

Rayner DCF. Optimization for Heuristic Search. [Internet] [Doctoral dissertation]. University of Alberta; 2014. [cited 2019 Oct 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

17. 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 October 22, 2019. 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 Oct 2019.

Vancouver:

Zhu W. Nonlocal Variational Methods in Image and Data Processing. [Internet] [Thesis]. UCLA; 2017. [cited 2019 Oct 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

Duke University

18. Wang, Ye. Bayesian Computation for High-Dimensional Continuous & Sparse Count Data .

Degree: 2018, Duke University

URL: http://hdl.handle.net/10161/16853

► Probabilistic modeling of multidimensional data is a common problem in practice. When the data is continuous, one common approach is to suppose that the…
(more)

Subjects/Keywords: Statistics; dimension reduction; high dimensional; manifold learning; MCMC; scalable inference

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APA (6^{th} Edition):

Wang, Y. (2018). Bayesian Computation for High-Dimensional Continuous & Sparse Count Data . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/16853

Not specified: Masters Thesis or Doctoral Dissertation

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

Wang, Ye. “Bayesian Computation for High-Dimensional Continuous & Sparse Count Data .” 2018. Thesis, Duke University. Accessed October 22, 2019. http://hdl.handle.net/10161/16853.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Wang, Ye. “Bayesian Computation for High-Dimensional Continuous & Sparse Count Data .” 2018. Web. 22 Oct 2019.

Vancouver:

Wang Y. Bayesian Computation for High-Dimensional Continuous & Sparse Count Data . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/10161/16853.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang Y. Bayesian Computation for High-Dimensional Continuous & Sparse Count Data . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/16853

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 (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 October 22, 2019. 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 Oct 2019.

Vancouver:

Venkataraman V. Advanced Machine Learning Approaches for Target Detection, Tracking and Recognition. [Internet] [Thesis]. Oklahoma State University; 2010. [cited 2019 Oct 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

Colorado State University

20.
Emerson, Tegan Halley.
Geometric data analysis approach to dimension reduction in machine *learning* and data mining in medical and biological sensing, A.

Degree: PhD, Mathematics, 2017, Colorado State University

URL: http://hdl.handle.net/10217/183941

► Geometric data analysis seeks to uncover and leverage structure in data for tasks in machine *learning* when data is visualized as points in some dimensional,…
(more)

Subjects/Keywords: dimension reduction; Grassmannian manifold; data mining; machine learning; geometric data analysis

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APA (6^{th} Edition):

Emerson, T. H. (2017). Geometric data analysis approach to dimension reduction in machine learning and data mining in medical and biological sensing, A. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/183941

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

Emerson, Tegan Halley. “Geometric data analysis approach to dimension reduction in machine learning and data mining in medical and biological sensing, A.” 2017. Doctoral Dissertation, Colorado State University. Accessed October 22, 2019. http://hdl.handle.net/10217/183941.

MLA Handbook (7^{th} Edition):

Emerson, Tegan Halley. “Geometric data analysis approach to dimension reduction in machine learning and data mining in medical and biological sensing, A.” 2017. Web. 22 Oct 2019.

Vancouver:

Emerson TH. Geometric data analysis approach to dimension reduction in machine learning and data mining in medical and biological sensing, A. [Internet] [Doctoral dissertation]. Colorado State University; 2017. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/10217/183941.

Council of Science Editors:

Emerson TH. Geometric data analysis approach to dimension reduction in machine learning and data mining in medical and biological sensing, A. [Doctoral Dissertation]. Colorado State University; 2017. Available from: http://hdl.handle.net/10217/183941

Princeton University

21.
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

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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 October 22, 2019. 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 Oct 2019.

Vancouver:

Holiday A. Manifold learning for coarse-graining networks and for parameter reduction . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2019 Oct 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

22. 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 October 22, 2019. 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 Oct 2019.

Vancouver:

Pozharskiy D. Two Studies of Complex Nonlinear Systems: Engineered Granular Crystals and Coarse-Graining Optimization Problems . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2019 Oct 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

23.
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 October 22, 2019. 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 Oct 2019.

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 2019 Oct 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

24. 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 (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 October 22, 2019. 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 Oct 2019.

Vancouver:

Fang C. Novel Frameworks for Mining Heterogeneous and Dynamic Networks. [Internet] [Doctoral dissertation]. University of Cincinnati; 2011. [cited 2019 Oct 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

25.
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 October 22, 2019. 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 Oct 2019.

Vancouver:

Ramirez Jr. J. Learning from Manifold-Valued Data: An Application to Seismic Signal Processing. [Internet] [Masters thesis]. University of Colorado; 2012. [cited 2019 Oct 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

Universitat Politècnica de Catalunya

26.
Cruz Barbosa, Raúl.
Generative *manifold* *learning* for the exploration of partially labeled data.

Degree: Departament de Llenguatges i Sistemes Informàtics, 2009, Universitat Politècnica de Catalunya

URL: http://hdl.handle.net/10803/78053

► Resum de la tesi (màxim 4000 caràcters. Si se supera aquest límit, el resum es tallarà automàticament al caràcter 4000) En muchos problemas de aplicación…
(more)

Subjects/Keywords: Semi-supervised learning; Generative topographic mapping; Geodesic distance; Manifold learning; Clustering; Visualitzation; Classification; 004

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APA (6^{th} Edition):

Cruz Barbosa, R. (2009). Generative manifold learning for the exploration of partially labeled data. (Thesis). Universitat Politècnica de Catalunya. Retrieved from http://hdl.handle.net/10803/78053

Not specified: Masters Thesis or Doctoral Dissertation

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

Cruz Barbosa, Raúl. “Generative manifold learning for the exploration of partially labeled data.” 2009. Thesis, Universitat Politècnica de Catalunya. Accessed October 22, 2019. http://hdl.handle.net/10803/78053.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Cruz Barbosa, Raúl. “Generative manifold learning for the exploration of partially labeled data.” 2009. Web. 22 Oct 2019.

Vancouver:

Cruz Barbosa R. Generative manifold learning for the exploration of partially labeled data. [Internet] [Thesis]. Universitat Politècnica de Catalunya; 2009. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/10803/78053.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cruz Barbosa R. Generative manifold learning for the exploration of partially labeled data. [Thesis]. Universitat Politècnica de Catalunya; 2009. Available from: http://hdl.handle.net/10803/78053

Not specified: Masters Thesis or Doctoral Dissertation

27. 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 October 22, 2019. 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 Oct 2019.

Vancouver:

SHENG Z. Exploring face space: A computational approach. [Internet] [Thesis]. National University of Singapore; 2007. [cited 2019 Oct 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

28.
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

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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 October 22, 2019. 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 Oct 2019.

Vancouver:

Wang C. A Geometric Framework for Transfer Learning Using Manifold Alignment. [Internet] [Doctoral dissertation]. U of Massachusetts : PhD; 2010. [cited 2019 Oct 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

29.
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

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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 October 22, 2019. https://scholarworks.rit.edu/theses/10122.

MLA Handbook (7^{th} Edition):

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

Vancouver:

Minnehan BL. Deep Grassmann Manifold Optimization for Computer Vision. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2019. [cited 2019 Oct 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

30. 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/

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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 October 22, 2019. 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 Oct 2019.

Vancouver:

Wang H. Objects Extraction and Recognition for Camera-Based Interaction: Heuristic and Statistical Approaches. [Internet] [Thesis]. Helsinki University of Technology; 2008. [cited 2019 Oct 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