Tenenbaum , Dimensionality Reduction A Global Geometric Framework for Nonlinear

نویسنده

  • Joshua B. Tenenbaum
چکیده

www.sciencemag.org (this information is current as of February 19, 2007 ): The following resources related to this article are available online at http://www.sciencemag.org/cgi/content/full/290/5500/2319 version of this article at: including high-resolution figures, can be found in the online Updated information and services, http://www.sciencemag.org/cgi/content/full/290/5500/2319/DC1 can be found at: Supporting Online Material http://www.sciencemag.org/cgi/content/full/290/5500/2319#otherarticles , 11 of which can be accessed for free: cites 14 articles This article 300 article(s) on the ISI Web of Science. cited by This article has been http://www.sciencemag.org/cgi/content/full/290/5500/2319#otherarticles 24 articles hosted by HighWire Press; see: cited by This article has been http://www.sciencemag.org/cgi/collection/psychology Psychology : subject collections This article appears in the following http://www.sciencemag.org/help/about/permissions.dtl in whole or in part can be found at: this article permission to reproduce of this article or about obtaining reprints Information about obtaining

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Joshua B . Tenenbaum Reduction A Global Geometric Framework for Nonlinear Dimensionality

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A global geometric framework for nonlinear dimensionality reduction.

Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimension...

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Global Versus Local Methods in Nonlinear Dimensionality Reduction

Recently proposed algorithms for nonlinear dimensionality reduction fall broadly into two categories which have different advantages and disadvantages: global (Isomap [1]), and local (Locally Linear Embedding [2], Laplacian Eigenmaps [3]). We present two variants of Isomap which combine the advantages of the global approach with what have previously been exclusive advantages of local methods: c...

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Joshua B . Tenenbaum , Dimensionality Reduction A Global Geometric Framework for Nonlinear

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تاریخ انتشار 2007