Nonlinear dimensionality reduction

Results: 210



#Item
31Global versus local methods in nonlinear dimensionality reduction Vin de Silva Department of Mathematics, Stanford University,

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
    32ROBERTO BATTITI, MAURO BRUNATO. The LION Way: Machine Learning plus Intelligent Optimization. LIONlab, University of Trento, Italy, Apr 2015 http://intelligentoptimization.org/LIONbook

    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
    33Regularizers 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

    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
    34Large-Scale Manifold Learning Ameet Talwalkar Courant Institute New York, NY Sanjiv Kumar

    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
    35LETTER Communicated by Joshua B. Tenenbaum Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

    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
    36REPORTS 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

    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
    37Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference David Mimno Dept. of Information Science Cornell University Ithaca, NY, 14853

    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
    38Learning to Disentangle Factors of Variation with Manifold Interaction Scott Reed REEDSCOT @ UMICH . EDU Kihyuk Sohn KIHYUKS @ UMICH . EDU

    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
    39slideColor, Geometric Methods and Manifold Learning Mikhail Belkin, Ohio State University, prepared jointly with Partha Niyogi

    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