Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions

Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Volumn , Issue , 2011, Pages 1129-1136

  Agarwal, Alekh ^a   Negahban, Sahand ^a   Wainwright, Martin J ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX OPTIMIZATION PROBLEMS; CONVEX RELAXATION; ERROR BOUND; FROBENIUS NORM; HIGH DIMENSIONS; HIGH-DIMENSIONAL; LOW RANK; LOW-DIMENSIONAL STRUCTURES; LOW-RANK MATRICES; MATRIX; MATRIX DECOMPOSITION; MINIMAX; NOISY MATRIX; OPTIMAL RATE; ROBUST PCA; STOCHASTIC NOISE; THEORETICAL PREDICTION; UPPER BOUND;

CONVEX OPTIMIZATION; ERROR ANALYSIS; LEARNING SYSTEMS; OPTIMIZATION;

RELAXATION PROCESSES;

EID: 80053444305     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

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