Compressive classification via secant projections

Yun Li, Chinmay Hegde, Richard G. Baraniuk, Kevin F. Kelly

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    One novel dimensional reduction method based on manifold modeling is applied on Rice single pixel camera for targets classification. The number of measurements is at least halved compared with equivalent classification result via random projections.

    Original languageEnglish (US)
    Title of host publicationComputational Optical Sensing and Imaging, COSI 2013
    PublisherOptical Society of America
    ISBN (Print)9781557529756
    StatePublished - Jan 1 2013
    EventComputational Optical Sensing and Imaging, COSI 2013 - Arlington, VA, United States
    Duration: Jun 23 2013Jun 27 2013

    Publication series

    NameOptics InfoBase Conference Papers
    ISSN (Electronic)2162-2701

    Conference

    ConferenceComputational Optical Sensing and Imaging, COSI 2013
    CountryUnited States
    CityArlington, VA
    Period6/23/136/27/13

    ASJC Scopus subject areas

    • Instrumentation
    • Atomic and Molecular Physics, and Optics

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  • Cite this

    Li, Y., Hegde, C., Baraniuk, R. G., & Kelly, K. F. (2013). Compressive classification via secant projections. In Computational Optical Sensing and Imaging, COSI 2013 (Optics InfoBase Conference Papers). Optical Society of America.