A reweighted nuclear norm minimization algorithm for low rank matrix recovery

• Autores: Yu-Fan Li, Yan-Jiao Zhang, Zheng-Hai Huang
• Localización: Journal of computational and applied mathematics, ISSN 0377-0427, Vol. 263, Nº 1, 2014, págs. 338-350
• Idioma: inglés
• DOI: 10.1016/j.cam.2013.12.005
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• Resumen

  • In this paper, we propose a reweighted nuclear norm minimization algorithm based on the weighted fixed point method (RNNM�WFP algorithm) to recover a low rank matrix, which iteratively solves an unconstrained L2�Mp minimization problem introduced as a nonconvex smooth approximation of the low rank matrix minimization problem.Weprove that any accumulation point of the sequence generated by the RNNM�WFP algorithm is a stationary point of the L2�Mp minimization problem. Numerical experiments on randomly generated matrix completion problems indicate that the proposed algorithm has better recoverability compared to existing iteratively reweighted algorithms.