Expectation-Maximization approaches to independent component analysis

Expectation-Maximization (EM) algorithms for independent component analysis are presented in this paper. For super-Gaussian sources, a variational method is employed to develop an EM algorithm in closed form for learning the mixing matrix and inferring the independent components. For sub-Gaussian sources, a symmetrical form of the Pearson mixture model (Neural Comput. 11 (2) (1999) 417-441) is used as the prior, which also enables the development of an EM algorithm in fclosed form for parameter estimation.

[1]  J. Cardoso,et al.  Maximum Likelihood Source SeparationBy the Expectation-Maximization Technique : Deterministic and Stochastic Implementation , 1995 .

[2]  Michael S. Lewicki,et al.  Efficient coding of natural sounds , 2002, Nature Neuroscience.

[3]  Michael I. Jordan,et al.  Variational methods for inference and estimation in graphical models , 1997 .

[4]  Mark A. Girolami,et al.  A Variational Method for Learning Sparse and Overcomplete Representations , 2001, Neural Computation.

[5]  Terrence J. Sejnowski,et al.  Learning Overcomplete Representations , 2000, Neural Computation.

[6]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[7]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[8]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[9]  P. Pajunen,et al.  Blind separation of binary sources with less sensors than sources , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[10]  Ole Winther,et al.  Mean-Field Approaches to Independent Component Analysis , 2002, Neural Computation.

[11]  Wenyu Liu,et al.  Blind source separation of more sources than mixtures using generalized exponential mixture models , 2004, Neurocomputing.

[12]  Hongjun Chen,et al.  An EM algorithm for learning sparse and overcomplete representations , 2004, Neurocomputing.

[13]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.