Journal
PATTERN RECOGNITION LETTERS
Volume 33, Issue 9, Pages 1163-1170Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2012.02.007
Keywords
Dimensionality reduction; Sparse models; PCA; Supervised learning; Un-supervised learning; Compressive sensing
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Funding
- Charles Stint University [OPA 4818]
- Australian Government
- Australian Research Council through the ICT Centre of Excellence
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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. (C) 2012 Elsevier B.V. All rights reserved.
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