Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/72921
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Type: Journal article
Title: Dimensionality reduction via compressive sensing
Author: Gao, J.
Shi, Q.
Caetano, T.
Citation: Pattern Recognition Letters, 2012; 33(9):1163-1170
Publisher: Elsevier Science BV
Issue Date: 2012
ISSN: 0167-8655
1872-7344
Statement of
Responsibility: 
Junbin Gao, Qinfeng Shi and Tibério S. Caetano
Abstract: Compressive sensing is an emerging field predicated upon the fact that, if a signal has a sparse representation in some basis, then it can be almost exactly reconstructed from very few random measurements. Many signals and natural images, for example under the wavelet basis, have very sparse representations, thus those signals and images can be recovered from a small amount of measurements with very high accuracy. This paper is concerned with the dimensionality reduction problem based on the compressive assumptions. We propose novel unsupervised and semi-supervised dimensionality reduction algorithms by exploiting sparse data representations. The experiments show that the proposed approaches outperform state-of-the-art dimensionality reduction methods. © 2012 Elsevier B.V. All rights reserved.
Rights: © 2012 Elsevier B.V. All rights reserved.
DOI: 10.1016/j.patrec.2012.02.007
Appears in Collections:Aurora harvest 5
Computer Science publications

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