Adaptive constrained learning in reproducing kernel hilbert spaces: The robust beamforming case

IEEE Transactions on Signal Processing

Volumn 57, Issue 12, 2009, Pages 4744-4764

  Slavakis, Konstantinos ^a   Theodoridis, Sergios ^b   Yamada, Isao ^c  

^a UNIVERSITY OF PELOPONNESE   (Greece)

^b UNIVERSITY OF ATHENS   (Greece)

^c TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Adaptive learning; Beamforming; Convex analysis; Fixed point set; Reproducing kernel Hilbert space (RKHS)

Indexed keywords

ADAPTIVE BEAMFORMING; ADAPTIVE LEARNING; ADAPTIVE PROJECTED SUBGRADIENT METHODS; APRIORI; BEAM FORMERS; BEAM PATTERN; CLOSED CONVEX SETSS; CONVEX ANALYSIS; GENERAL NATURE; KERNEL REGRESSION; LINEAR COMPLEXITY; LOSS FUNCTIONS; MIN-MAX OPTIMIZATION; NONLINEAR LEARNING; NUMERICAL EXAMPLE; POINT SET; REPRODUCING KERNEL HILBERT SPACES; ROBUST BEAMFORMING; ROBUST STATISTICS; SIMPLE EXPRESSION; STEERING VECTOR ERRORS; STRONG CONVERGENCE; TRAINING DATA; UNKNOWN PARAMETERS;

BEAMFORMING; COMPUTATION THEORY; CONSTRAINED OPTIMIZATION; EDUCATION; GEOMETRY; LEARNING ALGORITHMS; SET THEORY; SPECIFICATIONS;

HILBERT SPACES;

EID: 70450233513     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2009.2027771     Document Type: Article

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