Block Jacobi-type methods for log-likelihood based linear independent subspace analysis

Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP

Volumn , Issue , 2007, Pages 133-138

  Shen, Hao ^a,b   Hüper, Knut ^a,b   Kleinsteuber, Martin ^c  

^a CSIRO   (Australia)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c UNIVERSITY OF WÜRZBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONVERGENCE OF NUMERICAL METHODS; LEARNING SYSTEMS; ROBOT LEARNING; SEPARATION; SIGNAL PROCESSING; STATISTICAL METHODS;

CONTRAST FUNCTIONS; IEEE SIGNAL PROCESSING SOCIETY; INDEPENDENT COMPONENT ANALYSIS (ICA); INDEPENDENT SUBSPACE ANALYSIS; INVARIANT FEATURES; ISA METHODS; LOCAL CONVERGENCE; LOCAL QUADRATIC CONVERGENCE; LOG-LIKELIHOOD; MACHINE-LEARNING; NONPARAMETRIC APPROACHES; NUMERICAL EXPERIMENTS; RIGOROUS ANALYSIS; STATISTICAL DEPENDENCE; STATISTICAL INDEPENDENCE; TYPE METHODS;

INDEPENDENT COMPONENT ANALYSIS;

EID: 48149108766     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2007.4414295     Document Type: Conference Paper

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