Fast Kernel Sparse Representation

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Title Fast Kernel Sparse Representation
Author Li, Hanxi; Gao, Yongsheng; Sun, Jun
Publication Title Proceedings - 2011 International Conference on Digital Image Computing: Techniques and Applications DICTA 2011
Editor IEEE
Year Published 2011
Place of publication Los Alamitos, CA, USA
Publisher IEEE Computer Society
Abstract Two efficient algorithms are proposed to seek the sparse representation on high-dimensional Hilbert space. By proving that all the calculations in Orthogonal Match Pursuit (OMP) are essentially inner-product combinations, we modify the OMP algorithm to apply the kernel-trick. The proposed Kernel OMP (KOMP) is much faster than the existing methods, and illustrates higher accuracy in some scenarios. Furthermore, inspired by the success of group-sparsity, we enforce a rigid group-sparsity constraint on KOMP which leads to a noniterative variation. The constrained cousin of KOMP, dubbed as Single-Step KOMP (S-KOMP), merely takes one step to achieve the sparse coefficients. A remarkable improvement (up to 2,750 times) in efficiency is reported for S-KOMP, with only a negligible loss of accuracy.
Peer Reviewed Yes
Published Yes
Alternative URI http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6128662
ISBN 978-0-7695-4588-2
Conference name 2011 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2011)
Location Noosa, Queensland, Australia
Date From 2011-12-06
Date To 2011-12-08
URI http://hdl.handle.net/10072/42997
Date Accessioned 2012-02-03; 2012-02-20T05:51:30Z
Date Available 2012-02-20T05:51:30Z
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Computer Vision; Pattern Recognition and Data Mining
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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