MULTI-SCALE KERNEL BASIS AND ITERATIVE ORTHOGONAL MATCHING PURSUIT FOR SPARSE APPROXIMATION

被引：0

作者：

Xie, Zhi-Peng ^{[1
,2
]}

Chen, Song-Can ^{[1
]}

Wu, Yang-Yang ^{[2
]}

Chen, Duan-Sheng ^{[2
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Dept Comp Sci & Engn, Nanjing 210016, Peoples R China

[2] Huaqiao Univ, Coll Comp Sci & Technol, Quanzhou 362021, Fujian, Peoples R China

来源：

PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年

关键词：

Cardinal spine kernel basis; Translation invariant fast decreasing kernel basis; Orthogonal Matching Pursuit; Iterative update; Sparse representation; REGRESSION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Function basis and approximation algorithm are two key elements in sparse representation. In this paper, some cardinal spline kernel basis and translation invariant fast decreasing kernel basis are presented, an iterative orthogonal matching pursuit algorithm(IOMP) is proposed, which is based on iterative update of hermitian inverse matrix. Experiments and comparisons on sparse representation of signal and regression datasets demonstrate that the proposed multi-scale kernel basis and iterative orthogonal matching pursuit algorithm (IOMP) are good at fast computing sparse approximation.

引用

页码：1765 / +

页数：3

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