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You searched for subject:(Manifold learning). Showing records 1 – 30 of 110 total matches.

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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

 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 (6th 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 (16th 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 (7th 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

Subjects/Keywords: Manifold Learning

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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

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 (6th 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 (16th 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 (7th 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

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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Technology, Sydney

11. Zhou, T. Compressed learning.

Degree: 2013, University of Technology, Sydney

 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

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APA (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

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

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APA (6th 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 (16th 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 (7th 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

  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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

 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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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 Manifold-Valued Data: An Application to Seismic Signal Processing.

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

  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 (6th 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 (16th 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 (7th 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

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

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

APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

  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 (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

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

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

APA (6th 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/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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/.

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

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

 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

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

APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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