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Spectral clustering / Cluster analysis / Dimension reduction / Kernel trick / K-means clustering / Principal component analysis / Isomap / Graph partition / Mutual information / Statistics / Multivariate statistics / Nonlinear dimensionality reduction


An Iterative Locally Linear Embedding Algorithm
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Document Date: 2012-06-07 13:20:54


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Andrew Y. / Belhumeur / P. Gradient / Edinburgh / Arlington / /

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Newsgroup / Lee D.D. / LG / AT&T / MIT Press / Georghiades A. S. / Weston / Mnist Umist Binalpha Yale Caltec MSRC Newsgroup / Reuters / /

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Jordan / United Kingdom / Scotland / /

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The University of Texas / /

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Above dimension reduction algorithms / inner product / sparse similarity learning algorithm / use clustering algorithms / computational algorithm / sparse kernel using algorithm / learning algorithm / quadratic placement algorithm / /

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University of Texas at Arlington / National Science Foundation / MIT / Yale / National Academy of Science / Pattern Analysis and Machine Intelligence / /

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Kriegman / Heng Huang / Zha / Lal / Chris Ding / Silva / V / /

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D. J. / author / /

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Pentax K-x Digital Camera / Kij / K0 / /

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Texas / Kentucky / YT / P. N. / /

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IEEE Transactions on Pattern Analysis and Machine Intelligence / Machine Learning / /

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This algorithm / coherent iterative LLE algorithm / sparse similarity learning algorithm / sparse kernel using algorithm / Clustering Results We use clustering algorithms / proposed algorithms / Machine Learning / html / iterative LLE algorithm / continuous An Iterative Locally Linear Embedding Algorithm / Locally Linear Embedding Algorithm / sparse W-learning algorithm / learning algorithm / final iterative LLE algorithm / improved W-learning algorithm / quadratic placement algorithm / W-learning algorithm / Learning algorithms / similarity Algorithm / Above dimension reduction algorithms / computational algorithm / 1 Iterative LLE algorithm / /

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