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Robust Matrix Completion via Maximum Correntropy Criterion and Half-Quadratic Optimization
Alternative TitleWOS:000506362200002
He, Yicong; Wang, Fei; Li, Yingsong1,2; Qin, Jing3; Chen, Badong
Source PublicationIEEE TRANSACTIONS ON SIGNAL PROCESSING
2020
Volume68Pages:181-195
DOI10.1109/TSP.2019.2952057
ISSN1053-587X
Language英语
KeywordSignal processing algorithms Kernel Measurement uncertainty Cost function Minimization Linear programming Matrix completion matrix factorization robust methods correntropy FACTORIZATION MINIMIZATION APPROXIMATION SIGNAL
AbstractRobust matrix completion aims to recover a low-rank matrix from a subset of noisy entries perturbed by complex noises. Traditional matrix completion algorithms are always based on -norm minimization and are sensitive to non-Gaussian noise with outliers. In this paper, we propose a novel robust and fast matrix completion method based on the maximum correntropy criterion (MCC). The correntropy-based error measure is utilized instead of the -based error norm to improve robustness against noise. By using the half-quadratic optimization technique, the correntropy-based optimization can be transformed into a weighted matrix factorization problem. Two efficient algorithms are then derived: an alternating minimization-based algorithm and an alternating gradient descent-based algorithm. These algorithms do not require the singular value decomposition (SVD) to be calculated for each iteration. Furthermore, an adaptive kernel width selection strategy is proposed to accelerate the convergence speed as well as improve the performance. A comparison with existing robust matrix completion algorithms is provided by simulations and shows that the new methods can achieve better performance than the existing state-of-the-art algorithms.
Indexed BySCI
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Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7615
Collection中国科学院国家空间科学中心
Affiliation1.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Shaanxi, Peoples R China
2.Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 150001, Heilongjiang, Peoples R China
3.Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China
4.Hong Kong Polytech Univ, Ctr Smart Hlth, Sch Nursing, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
He, Yicong,Wang, Fei,Li, Yingsong,et al. Robust Matrix Completion via Maximum Correntropy Criterion and Half-Quadratic Optimization[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING,2020,68:181-195.
APA He, Yicong,Wang, Fei,Li, Yingsong,Qin, Jing,&Chen, Badong.(2020).Robust Matrix Completion via Maximum Correntropy Criterion and Half-Quadratic Optimization.IEEE TRANSACTIONS ON SIGNAL PROCESSING,68,181-195.
MLA He, Yicong,et al."Robust Matrix Completion via Maximum Correntropy Criterion and Half-Quadratic Optimization".IEEE TRANSACTIONS ON SIGNAL PROCESSING 68(2020):181-195.
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