Performance guarantees of transformed Schatten-1 regularization for exact low-rank matrix recovery

• Published in: International journal of machine learning and cybernetics Vol. 12; no. 12; pp. 3379 - 3395
• Main Authors: Wang, Zhi, Hu, Dong, Luo, Xiaohu, Wang, Wendong, Wang, Jianjun, Chen, Wu
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Complex Systems; Computational Intelligence; Control; Convergence; Engineering; Guarantees; Image processing; Mathematical analysis; Mechatronics; Optimization; Original Article; Pattern Recognition; Penalty function; Recommender systems; Recovery; Regularization; Regularization methods; Robotics; Systems Biology; Nonconvex model; Equivalence; Transformed Schatten-1 penalty function; Low-rank matrix recovery

Low-rank matrix recovery aims to recover a matrix of minimum rank that subject to linear system constraint. It arises in various real world applications, such as recommender systems, image processing, and deep learning. Inspired by compressive sensing, the rank minimization can be relaxed to nuclear...