Two dictionaries matching pursuit for sparse decomposition of signals

Authors:
Peng Xu;Dezhong Yao
Affiliations:
Center of Neuroinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, ChengDu, SiChuan, China;Center of Neuroinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, ChengDu, SiChuan, China
Venue:
Signal Processing - Special section: Distributed source coding
Year:
2006

Citing 4
Cited 1

Atomic Decomposition by Basis Pursuit

SIAM Journal on Scientific Computing
JPEG2000: the upcoming still image compression standard

Pattern Recognition Letters
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information

Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Matching pursuits with a wave-based dictionary

IEEE Transactions on Signal Processing

Two-dimensional random projection

Signal Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Signal may be decomposed sparsely and power focally in an over-complete dictionary with matching pursuit (MP). In this paper, proposed is a modified MP method named two dictionaries MP (TDMP) to decompose signal more sparsely. In the iteration procedure of TDMP, the over-complete dictionary is classified into two separate dictionaries with the selected and unselected atoms, and in each iteration, the algorithm was designed to have more chances than the original MP to choose the atom in the selected atom dictionary as the optimal atom by the constraint of a simulate annealing threshold function, thus the algorithm avails for a more sparse decomposition. The decomposition results for a cosine-modulated exponential signal and an actual speech signal showed that the proposed TDMP could decompose signal more sparsely.