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Publications [#265089] of Guillermo Sapiro

Papers Published

  1. Ma, Y; Niyogi, P; Sapiro, G; Vidal, R, Dimensionality reduction via subspace and submanifold learning, Ieee Signal Processing Magazine, vol. 28 no. 2 (January, 2011), pp. 14-126, Institute of Electrical and Electronics Engineers (IEEE), ISSN 1053-5888 [doi]
    (last updated on 2019/06/18)

    The problem of finding and exploiting low-dimensional structures in high-dimensional data is taking on increasing importance in image, video, or audio processing; Web data analysis/search; and bioinformatics, where data sets now routinely lie in observational spaces of thousands, millions, or even billions of dimensions. The curse of dimensionality is in full play here: We often need to conduct meaningful inference with a limited number of samples in a very high-dimensional space. Conventional statistical and computational tools have become severely inadequate for processing and analyzing such high-dimensional data. © 2006 IEEE.
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Mathematics Department
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