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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 November 22, 2019. 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 Nov 2019.

Vancouver:

Ahmed, Talal 1. Geometric manifold approximation using locally linear approximations. [Internet] [Masters thesis]. Rutgers University; 2016. [cited 2019 Nov 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 November 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 (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 Nov 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 Nov 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 November 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 (7th Edition):

Flynn, John Joseph. “Learning a simplicial structure using sparsity.” 2014. Web. 22 Nov 2019.

Vancouver:

Flynn JJ. Learning a simplicial structure using sparsity. [Internet] [Thesis]. UCLA; 2014. [cited 2019 Nov 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 November 22, 2019. http://hdl.handle.net/1959.13/1407533.

MLA Handbook (7th Edition):

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

Vancouver:

Aziz MFB. Manifold alignment through deep autoencoders. [Internet] [Doctoral dissertation]. University of Newcastle; 2019. [cited 2019 Nov 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 November 22, 2019. 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 Nov 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 Nov 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 November 22, 2019. 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 Nov 2019.

Vancouver:

Kwok E. Dynamic Isoperimetry on Graphs and Weighted Riemannian manifolds. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2019 Nov 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 November 22, 2019. 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 Nov 2019.

Vancouver:

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

8. 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 November 22, 2019. 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 Nov 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 Nov 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

9. 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 November 22, 2019. 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 Nov 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 Nov 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

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 November 22, 2019. 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 Nov 2019.

Vancouver:

De Deuge M. Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies . [Internet] [Thesis]. University of Sydney; 2015. [cited 2019 Nov 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, Tianyi. 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, Tianyi. “Compressed learning.” 2013. Thesis, University of Technology, Sydney. Accessed November 22, 2019. 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, Tianyi. “Compressed learning.” 2013. Web. 22 Nov 2019.

Vancouver:

Zhou T. Compressed learning. [Internet] [Thesis]. University of Technology, Sydney; 2013. [cited 2019 Nov 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 November 22, 2019. 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 Nov 2019.

Vancouver:

Vladymyrov M. Large-Scale Methods for Nonlinear Manifold Learning. [Internet] [Thesis]. University of California – Merced; 2014. [cited 2019 Nov 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 November 22, 2019. http://hdl.handle.net/2047/D20260369.

MLA Handbook (7th Edition):

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

Vancouver:

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

14. 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 (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 November 22, 2019. 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 Nov 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 Nov 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

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

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 November 22, 2019. 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 Nov 2019.

Vancouver:

Tran L. High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach. [Internet] [Doctoral dissertation]. Old Dominion University; 2014. [cited 2019 Nov 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


EPFL

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

Degree: 2014, EPFL

 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 (6th 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

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

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

MLA Handbook (7th Edition):

de Morsier, Frank. “Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images.” 2014. Web. 22 Nov 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 Nov 22]. Available from: http://infoscience.epfl.ch/record/199126.

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

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


University of Washington

17. 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 November 22, 2019. http://hdl.handle.net/1773/40305.

MLA Handbook (7th Edition):

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

Vancouver:

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

18. 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 November 22, 2019. https://era.library.ualberta.ca/files/st74cs84d.

MLA Handbook (7th Edition):

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

Vancouver:

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

19. 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 November 22, 2019. 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 Nov 2019.

Vancouver:

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


Duke University

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

Degree: 2018, Duke University

  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 (6th Edition):

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

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

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

MLA Handbook (7th Edition):

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

Vancouver:

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

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

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


Oklahoma State University

21. 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 November 22, 2019. 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 Nov 2019.

Vancouver:

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


Colorado State University

22. 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

 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 (6th 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 (16th 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 November 22, 2019. http://hdl.handle.net/10217/183941.

MLA Handbook (7th 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 Nov 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 Nov 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

23. 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 November 22, 2019. 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 Nov 2019.

Vancouver:

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

24. 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 November 22, 2019. 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 Nov 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 Nov 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

25. 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 November 22, 2019. 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 Nov 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 Nov 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

26. 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 (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 November 22, 2019. 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 Nov 2019.

Vancouver:

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

27. 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 November 22, 2019. 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 Nov 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 Nov 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

28. 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

 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 (6th 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

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):

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

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

MLA Handbook (7th Edition):

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

Vancouver:

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

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

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

29. 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 (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 November 22, 2019. 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 Nov 2019.

Vancouver:

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

30. 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 November 22, 2019. 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 Nov 2019.

Vancouver:

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

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