An Improved ADM algorithm for RPCA optimization problem

Author

Chai Yi ; Xu Su ; Yin HongPeng

Author_Institution

Coll. of Autom. Eng., Chongqing Univ., Chongqing, China

fYear

2013

Firstpage

4476

Lastpage

4480

Abstract

This paper presents an improved alternating direction method (IADM) algorithm for robust principal component analysis (RPCA) optimization problem. Firstly distortion compensation technique is employed to convert 2-D real nature image to the sparse approximation matrix. Secondly an improved Singular Value Decomposition (block-SVD) is presented to converge to the better value than traditional alternating direction method (ADM). Finally, reconstructed image is built up by sparse and low-rank matrix. To illustrate the effectiveness of proposed approach, several experiments are conducted. Experimental results show that, compare with SVT, ALM, APG and ADM, the proposed approach has faster rate and better performance.

Keywords

approximation theory; image reconstruction; motion compensation; optimisation; principal component analysis; singular value decomposition; sparse matrices; 2D real nature image; IADM; RPCA optimization problem; block-SVD; distortion compensation technique; image reconstruction; improved ADM algorithm; improved alternating direction method algorithm; improved singular value decomposition; low-rank matrix; robust principal component analysis optimization problem; sparse approximation matrix; Algorithm design and analysis; Matrix decomposition; Noise; Optimization; Principal component analysis; Robustness; Sparse matrices; Alternating Direction Method; Robust Principal Component Analysis; Singular Value Decomposition;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2013 32nd Chinese

Conference_Location

Xi´an

Type

conf

Filename

6640208