Dimensionality reduction via compressive sensing
Date
2012
Authors
Gao, Junbin
Shi, Qinfeng
Caetano, Tiberio
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Elsevier
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.
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Keywords
Keywords: Compressive sensing; Dimensionality reduction; Dimensionality reduction algorithms; Dimensionality reduction method; Natural images; PCA; Random measurement; Semi-supervised; Sparse data; Sparse representation; Wavelet basis; Supervised learning; Signal r Compressive sensing; Dimensionality reduction; PCA; Sparse models; Supervised learning; Un-supervised learning
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Source
Pattern Recognition Letters
Type
Journal article
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2037-12-31
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