Dimensionality reduction via compressive sensing

0167-8655/$ see front matter 2012 Elsevier B.V. A doi:10.1016/j.patrec.2012.02.007 q This work is partially supported by Charles S Research Grant OPA 4818. 1 NICTA is funded by the Australian Government as re of Broadband, Communications and the Digital Econom Council through the ICT Centre of Excellence program. ⇑ Corresponding author. E-mail addresses: [email protected] (J. Gao), q [email protected] (T.S. Caetano). 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.