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Publications [#264701] of Guillermo Sapiro

Papers Published

  1. Qiu, Q; Sapiro, G, Learning compressed image classification features, 2014 Ieee International Conference on Image Processing, Icip 2014 (January, 2014), pp. 5761-5765, IEEE, ISBN 9781479957514 [doi]
    (last updated on 2019/06/18)

    © 2014 IEEE. Learning a transformation-based dimension reduction, thereby compressive, technique for classification is here proposed. High-dimensional data often approximately lie in a union of low-dimensional subspaces. We propose to perform dimension reduction by learning a 'fat' linear transformation matrix on subspaces using nuclear norm as the optimization criteria. The learned transformation enables dimension reduction, and, at the same time, restores a low-rank structure for data from the same class and maximizes the separation between different classes, thereby improving classification via learned low-dimensional features. Theoretical and experimental results support the proposed framework, which can be interpreted as learning compressing sensing matrices for classification.
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