Nonlinear dimensionality reduction

Results: 210



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31

Global versus local methods in nonlinear dimensionality reduction Vin de Silva Department of Mathematics, Stanford University,

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Source URL: pages.pomona.edu

Language: English
    32Vectors / Linear algebra / Dimension reduction / Multivariate statistics / Vector calculus / Principal component analysis / Euclidean vector / Vector space / Matrix / Projection / Random projection / Nonlinear dimensionality reduction

    ROBERTO BATTITI, MAURO BRUNATO. The LION Way: Machine Learning plus Intelligent Optimization. LIONlab, University of Trento, Italy, Apr 2015 http://intelligentoptimization.org/LIONbook

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    Source URL: intelligent-optimization.org

    Language: English - Date: 2015-10-06 09:20:21
    33Multivariate statistics / Dimension reduction / Matrix theory / Computational statistics / Linear algebra / Nonlinear dimensionality reduction / Bregman divergence / Semidefinite embedding / Principal component analysis / Statistics / Algebra / Mathematics

    Regularizers versus Losses for Nonlinear Dimensionality Reduction Yaoliang Yu, James Neufeld, Ryan Kiros, Xinhua Zhang, Dale Schuurmans Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8 Canada

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    Source URL: www.cs.toronto.edu

    Language: English - Date: 2013-12-26 22:59:57
    34Multivariate statistics / Matrix theory / Singular value decomposition / Dimension reduction / Nonlinear dimensionality reduction / Isomap / Semidefinite embedding / Eigenvalues and eigenvectors / Principal component analysis / Algebra / Mathematics / Linear algebra

    Large-Scale Manifold Learning Ameet Talwalkar Courant Institute New York, NY Sanjiv Kumar

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    Source URL: www.sanjivk.com

    Language: English - Date: 2010-06-01 18:50:24
    35Multivariate statistics / Multivariable calculus / Differential operators / Singular value decomposition / Dimension reduction / Laplace operator / Nonlinear dimensionality reduction / Laplacian matrix / Spectral clustering / Mathematical analysis / Mathematics / Calculus

    LETTER Communicated by Joshua B. Tenenbaum Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

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    Source URL: www2.imm.dtu.dk

    Language: English - Date: 2009-08-14 05:08:50
    36Computational statistics / Isomap / Nonlinear dimensionality reduction / Machine learning / Multidimensional scaling / Principal component analysis / Manifold / Geodesic / Differentiable manifold / Statistics / Multivariate statistics / Dimension reduction

    REPORTS 23; right 36, 13, and 27); superior frontal gyrus (left ⫺9, 31, and 45; right 17, 35, andAlthough the improvement in WM performance with cholinergic enhancement was a nonsignificant trend in the curre

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    Source URL: www2.imm.dtu.dk

    Language: English - Date: 2009-08-14 05:08:08
    37Multivariate statistics / Statistical natural language processing / Natural language processing / Computational linguistics / Dimension reduction / Principal component analysis / Nonlinear dimensionality reduction / Latent Dirichlet allocation / Randomized algorithm / Statistics / Mathematics / Probability and statistics

    Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference David Mimno Dept. of Information Science Cornell University Ithaca, NY, 14853

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    Source URL: mimno.infosci.cornell.edu

    Language: English - Date: 2014-10-16 01:54:56
    38Boltzmann machine / Multivariate statistics / Differential topology / Differential geometry / Manifold / Nonlinear dimensionality reduction / Partition function / Object recognition / Ludwig Boltzmann / Statistics / Topology / Physics

    Learning to Disentangle Factors of Variation with Manifold Interaction Scott Reed REEDSCOT @ UMICH . EDU Kihyuk Sohn KIHYUKS @ UMICH . EDU

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    Source URL: www-personal.umich.edu

    Language: English - Date: 2014-11-06 16:36:13
    39Differential geometry / Differential topology / Geometric topology / Manifold / Principal component analysis / Dimension reduction / Nonlinear dimensionality reduction / Calibrated geometry / Statistics / Multivariate statistics / Topology

    slideColor, Geometric Methods and Manifold Learning Mikhail Belkin, Ohio State University, prepared jointly with Partha Niyogi

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    Source URL: www2.imm.dtu.dk

    Language: English - Date: 2009-08-19 05:22:20
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