Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/72921
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
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 |
Published version: | http://dx.doi.org/10.1016/j.patrec.2012.02.007 |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.