Constrained Cramér-Rao Bound on Robust Principal Component Analysis

We investigate the behavior of the mean-square error (MSE) of low-rank and sparse matrix decomposition, in particular the special case of the robust principal component analysis (RPCA), and its generalization matrix completion and correction (MCC). We derive a constrained Cramér-Rao bound (CRB) for...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 59; H. 10; S. 5070 - 5076
Hauptverfasser:	Gongguo Tang, Nehorai, Arye
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.10.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accelerated proximal gradient algorithm Applied sciences Approximation constrained Cramér-Rao bound Constraints Cramer-Rao bounds Design engineering Detection, estimation, filtering, equalization, prediction Estimators Exact sciences and technology Information, signal and communications theory matrix completion and correction Matrix decomposition Maximum likelihood estimation mean-square error Modeling Optimization Principal component analysis robust principal component analysis Robustness Signal and communications theory Signal to noise ratio Signal, noise Sparse matrices Telecommunications and information theory Performance evaluation robust principal component analysis Matrix decomposition maximum likelihood estimation Mean square error Statistical method Unbiased estimation Sparse matrix Signal processing Maximum likelihood Accelerated proximal gradient algorithm Gradient method constrained Cramér―Rao bound matrix completion and correction Principal component analysis Cramer Rao inequality Signal to noise ratio mean-square error
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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