Compressive classification via secant projections

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

Compressive classification via secant projections

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Computational Optical Sensing and Imaging, COSI 2013
Publisher	Optical Society of America
ISBN (Print)	9781557529756
State	Published - Jan 1 2013
Event	Computational Optical Sensing and Imaging, COSI 2013 - Arlington, VA, United States Duration: Jun 23 2013 → Jun 27 2013

Other

Other	Computational Optical Sensing and Imaging, COSI 2013
Country	United States
City	Arlington, VA
Period	6/23/13 → 6/27/13

ASJC Scopus subject areas

Instrumentation
Atomic and Molecular Physics, and Optics

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title = "Compressive classification via secant projections",

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.",

author = "Yun Li and Chinmay Hegde and Baraniuk, {Richard G.} and Kelly, {Kevin F.}",

year = "2013",

month = jan,

day = "1",

language = "English (US)",

isbn = "9781557529756",

booktitle = "Computational Optical Sensing and Imaging, COSI 2013",

publisher = "Optical Society of America",

note = "Computational Optical Sensing and Imaging, COSI 2013 ; Conference date: 23-06-2013 Through 27-06-2013",

}

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AU - Li, Yun

AU - Hegde, Chinmay

AU - Baraniuk, Richard G.

AU - Kelly, Kevin F.

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N2 - 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.

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M3 - Conference contribution

SN - 9781557529756

BT - Computational Optical Sensing and Imaging, COSI 2013

PB - Optical Society of America

T2 - Computational Optical Sensing and Imaging, COSI 2013

Y2 - 23 June 2013 through 27 June 2013

ER -