Fast kernel sparse representation
Date
2011
Authors
Li, Hanxi
Gao, Yongsheng
Sun, Jun
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Publisher
IEEE Communications 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.
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Keywords
Keywords: Efficient algorithm; High-dimensional; Inner product; Kernel trick; Loss of accuracy; Non-iterative; One step; Orthogonal Matching Pursuit; Single-step; Sparse representation; Algorithms Kernel trick; Orthogonal Matching Pursuit; Sparse Representation
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A Novel Illumination-Invariant Loss for Monocular 3D
Pose Estimation
Type
Conference paper
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2037-12-31
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