Journal
SIGNAL PROCESSING
Volume 91, Issue 7, Pages 1589-1603Publisher
ELSEVIER
DOI: 10.1016/j.sigpro.2011.01.002
Keywords
Random projection; Concentration of measure; Sparse signal reconstruction
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Funding
- Iran Telecom Research Center (ITRC)
- Iran National Science Foundation (INSF)
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As an alternative to adaptive nonlinear schemes for dimensionality reduction, linear random projection has recently proved to be a reliable means for high-dimensional data processing. Widespread application of conventional random projection in the context of image analysis is, however, mainly impeded by excessive computational and memory requirements. In this paper, a two-dimensional random projection scheme is considered as a remedy to this problem, and the associated key notion of concentration of measure is closely studied. It is then applied in the contexts of image classification and sparse image reconstruction. Finally, theoretical results are validated within a comprehensive set of experiments with synthetic and real images. (C) 2011 Elsevier B.V. All rights reserved.
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