Hybridizing sparse component analysis with genetic algorithms for blind source separation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3745 LNBI, Issue , 2005, Pages 137-148

  Stadlthanner, Kurt ^a   Theis, Fabian J ^a   Puntonet, Carlos G ^b   Górriz, Juan M ^b   Tomé, Ana Maria ^c   Lang, Elmar W ^a  

^a UNIVERSITY OF REGENSBURG   (Germany)

^b UNIVERSITY OF GRANADA   (Spain)

^c UNIVERSITY OF AVEIRO   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

BLIND SOURCE SEPARATION; COMPUTER AIDED SOFTWARE ENGINEERING; COMPUTER SCIENCE; FUNCTIONS; INFORMATION TECHNOLOGY; PROBLEM SOLVING;

NMF ALGORITHMS; NONNEGATIVE BLIND SOURCE SEPARATION (BSS) PROBLEMS; NONNEGATIVE MATRIX FACTORIZATION (NMF); SPARSE COMPONENT ANALYSIS;

GENETIC ALGORITHMS;

EID: 33745326586     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11573067_15     Document Type: Conference Paper

References (4)

1. D.D. Lee and H.S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 40:788-791, 1999.
2. P.O. Hoyer. Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 5:1457-1469, 2004
3. A. Hyvärinen, Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neuronal Networks, 10(3), 626-634, 1999
4. A. Chipperfield, P. Fleming, H. Pohlheim, C. Fonseca, Genetic Algorithm Toolbox, Evolutionary Computation Research Group, University fo Sheffield, www.shef.ac.uk/acse/research/ecrg/