Developing Genetic Algorithms for Boolean Matrix Factorization

Matrix factorization or factor analysis is an important task helpful in the analysis of high dimensional real world data. There are sev- eral well known methods and algorithms for factorization of real data but many application areas including information retrieval, pattern recogni- tion and data mining require processing of binary rather than real data. Unfortunately, the methods used for real matrix factorization fail in the latter case. In this paper we introduce background and initial version of Genetic Algorithm for binary matrix factorization.

[1]  Michael W. Spratling.  Learning Image Components for Object Recognition , 2006, J. Mach. Learn. Res..

[2]  Michael W. Berry,et al.  Document clustering using nonnegative matrix factorization , 2006, Inf. Process. Manag..

[3]  Václav Snásel,et al.  Image Analysis by Methods of Dimension Reduction , 2007, 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07).

[4]  Michael W. Berry,et al.  Algorithms and applications for approximate nonnegative matrix factorization , 2007, Comput. Stat. Data Anal..

[5]  Ulrich Bodenhofer,et al.  Genetic Algorithms: Theory and Applications , 2002 .

[6]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[7]  Joos Vandewalle,et al.  A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..

[8]  Václav Snásel,et al.  Bars Problem Solving - New Neural Network Method and Comparison , 2007, MICAI.

[9]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[10]  M. Dianati,et al.  An Introduction to Genetic Algorithms and Evolution , 2002 .

[11]  D. O’Leary,et al.  Computation and Uses of the Semidiscrete Matrix Decomposition , 1999 .

[12]  P. Földiák,et al.  Forming sparse representations by local anti-Hebbian learning , 1990, Biological Cybernetics.

[13]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[14]  Chris H. Q. Ding,et al.  Binary Matrix Factorization with Applications , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[15]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[16]  M.W. Berry,et al.  Computational Methods for Intelligent Information Access , 1995, Proceedings of the IEEE/ACM SC95 Conference.

[17]  Václav Snásel,et al.  Binary Factor Analysis with Help of Formal Concepts , 2004, CLA.

[18]  B. Schölkopf,et al.  Modeling Dyadic Data with Binary Latent Factors , 2007 .

[19]  Gareth Jones,et al.  Genetic and Evolutionary Algorithms , 2002 .

[20]  Ales Keprt.  Using Blind Search and Formal Concepts for Binary Factor Analysis , 2004, DATESO.