Independent Component Analysis Algorithms and Applications
Dr. Te-Won Lee
Salk Institute, La Jolla, CA
Independent Component Analysis (ICA) is a method for extracting independent sour ces given only mixtures of the unknown sources. This method has a wide range of applications in signal- and image processing issues and it has been suggested th at ICA may reveal information about sensory coding strategies in the brain. The goal of my talk is to introduce ICA algorithms, to give examples of applicat ions in biomedical data analysis, and to summarize my most recent work in ICA mi xture models. I will present the assumptions in ICA and explain how algorithms can be derived from different theoretical principles such as information maximization, maximum likelihood and cummulants maximization. The algorithms are applied to sensory in formation from the brain such as electroencephalographic data and functional mag netic resonance imaging data. The results suggest that ICA can extract significant signals that were hidden in the observed data. The ICA mixture model is a way of extending the ICA formulati on to classification problems. It can be applied to learn the underlying structu res of images and sounds. The obtained image codes are used for image processing issues such as unsupervised classification, segmentation, de-noising, compressio n, and hyperspectral color image representation. The ICA codes learned for natur al images resemble the localized and oriented receptive fields found in the visu al cortex of mammals and the codes for hyperspectral images are related to human cone sensitivities. For speech signals we obtain codes that can be used for robu st speech recognition. ICA provides an interesting approach to data representation problems as well as new insights for coding sensory information in the brain. Te-Won Lee ---------- Te-Won Lee was born in Chungnam, Korea in 1969. He received his diploma degree in March 1995 and his Ph.D. degree in October 1997 with highest honor in electri cal engineering from the University of Technology Berlin. He was a visiting grad uate student at the Institute Nationale Polytechnique de Grenoble, the Universit y of California at Berkeley, and the Carnegie Mellon University. From 1995 to 1997 he was a Max-Planck Institute fellow. He is currently a Resea rch Associate with the Computational Neurobiology Laboratory at The Salk Institu te and a Research Assistant Professor at the Institute for Neural Computation at the University of California, San Diego. His research interests are in the areas of unsupervised learning algorithms, art ificial neural networks and Bayesian probability theory with applications to bio medical signal processing and data mining.
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Last update: October 2, 2000