Abstract:
We study the problem of low-rank and sparse decomposition from possibly noisy observations. We propose a novel objective function with nuclear norm on the low-rank term a...Show MoreMetadata
Abstract:
We study the problem of low-rank and sparse decomposition from possibly noisy observations. We propose a novel objective function with nuclear norm on the low-rank term and ℓ0-`norm' on the sparse term, as well as ℓ1-norm on the additive noise term. When there is no dense inlier noise, the proposed method shares the same theoretical guarantee as the Principal Component Pursuit (PCP), i.e., it can recover the low-rank component and sparse component exactly with high probability. Simulations in the noisy case demonstrate that the proposed method outperforms existing state-of-the-art methods. Results on a surveillance video application further verify the effectiveness of the proposed method.
Published in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 05-09 March 2017
Date Added to IEEE Xplore: 19 June 2017
ISBN Information:
Electronic ISSN: 2379-190X