Dimensionality reduction via compressive sensing

Junbin Gao, Qinfeng Shi, Tiberio S. Caetano

    Research output: Contribution to journalArticlepeer-review

    28 Citations (Scopus)

    Abstract

    Compressive sensing is an emerging field predicated upon the fact that, if a signal has a sparse representationin 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 highaccuracy. This paper is concerned with the dimensionality reduction problem based on the compressiveassumptions. We propose novel unsupervised and semi-supervised dimensionality reduction algorithmsby exploiting sparse data representations. The experiments show that the proposed approaches outperformstate-of-the-art dimensionality reduction methods.
    Original languageEnglish
    Pages (from-to)1163-1170
    Number of pages8
    JournalPattern Recognition Letters
    Volume33
    Issue number1
    DOIs
    Publication statusPublished - Jul 2012

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