An Alternative Perspective on Adaptive Independent Component Analysis Algorithms

This article develops an extended independent component analysis algorithm for mixtures of arbitrary subgaussian and supergaussian sources. The gaussian mixture model of Pearson is employed in deriving a closed-form generic score function for strictly subgaussian sources. This is combined with the score function for a unimodal supergaussian density to provide a computationally simple yet powerful algorithm for performing independent component analysis on arbitrary mixtures of nongaussian sources.

[1]  K. Pearson.  Contributions to the Mathematical Theory of Evolution , 1894 .

[2]  Brian Everitt,et al.  Cluster analysis , 1974 .

[3]  E. Oja.  Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[4]  Erkki Oja,et al.  Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..

[5]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[6]  A. W. Kemp,et al.  Kendall's Advanced Theory of Statistics. , 1994 .

[7]  Juha Karhunen,et al.  Representation and separation of signals using nonlinear PCA type learning , 1994, Neural Networks.

[8]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[9]  Andrzej Cichocki,et al.  Robust learning algorithm for blind separation of signals , 1994 .

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

[11]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[12]  Sailes K. Sengijpta.  Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

[13]  Erkki Oja,et al.  Simple Neuron Models for Independent Component Analysis , 1996, Int. J. Neural Syst..

[14]  Barak A. Pearlmutter,et al.  A Context-Sensitive Generalization of ICA , 1996 .

[15]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[16]  Andrzej Cichocki,et al.  Neural network approach to blind separation and enhancement of images , 1996, 1996 8th European Signal Processing Conference (EUSIPCO 1996).

[17]  Juha Karhunen,et al.  Neural approaches to independent component analysis and source separation , 1996, ESANN.

[18]  Shun-ichi Amari,et al.  Blind source separation-semiparametric statistical approach , 1997, IEEE Trans. Signal Process..

[19]  Erkki Oja,et al.  The nonlinear PCA learning rule in independent component analysis , 1997, Neurocomputing.

[20]  C. Fyfe,et al.  Generalised independent component analysis through unsupervised learning with emergent Bussgang properties , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[21]  Zied Malouche,et al.  A linear adaptive neural network for extraction of independent components , 1997, ESANN.

[22]  Shun-ichi Amari,et al.  Independent component analysis by the information-theoretic approach with mixture of densities , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[23]  Tzyy-Ping Jung,et al.  Extended ICA Removes Artifacts from Electroencephalographic Recordings , 1997, NIPS.

[24]  Eric Moulines,et al.  Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[25]  Julia Iiarhunen,et al.  BLIND SOURCE SEPARATION USING LEAST-SQUARES TYPE ADAPTIVE ALGORITHMS , 1997 .

[26]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[27]  Mark Girolami,et al.  Extraction of independent signal sources using a deflationary exploratory projection pursuit network , 1997 .

[28]  S. Amari,et al.  Multichannel blind separation and deconvolution of sources with arbitrary distributions , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[29]  Shun-ichi Amari,et al.  Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.

[30]  S Makeig,et al.  Spatially independent activity patterns in functional MRI data during the stroop color-naming task. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Andrzej Cichocki,et al.  A common neural-network model for unsupervised exploratory data analysis and independent component analysis , 1998, IEEE Trans. Neural Networks.

[32]  Mark A. Girolami,et al.  A nonlinear model of the binaural cocktail party effect , 1998, Neurocomputing.

[33]  A. J. Bell,et al.  A Unifying Information-Theoretic Framework for Independent Component Analysis , 2000 .

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

[35]  T. Sejnowski,et al.  Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects , 2000, Clinical Neurophysiology.