Stable Principal Component Pursuit via Convex Analysis | Zendy

Yin Lei | Zendy; Ankit Parekh | Zendy; Ivan Selesnick | Zendy

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Stable Principal Component Pursuit via Convex Analysis

Author(s) -

Yin Lei,

Ankit Parekh,

Ivan Selesnick

Publication year - 2019

Publication title -

ieee transactions on signal processing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.638

H-Index - 270

eISSN - 1941-0476

pISSN - 1053-587X

DOI - 10.1109/tsp.2019.2907264

Subject(s) - mathematical optimization , convex optimization , mathematics , matrix norm , robust principal component analysis , regularization (linguistics) , minimax , principal component analysis , convex function , convexity , separable space , convex analysis , penalty method , regular polygon , algorithm , computer science , artificial intelligence , eigenvalues and eigenvectors , mathematical analysis , physics , geometry , statistics , quantum mechanics , financial economics , economics

This paper aims to recover a low-rank matrix and a sparse matrix from their superposition observed in additive white Gaussian noise by formulating a convex optimization problem with a non-separable non-convex regularization. The proposed nonconvex penalty function extends the recent work of a multivariate generalized minimax-concave penalty for promoting sparsity. It avoids underestimation charact...

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