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Tutorial
May 28 , 2006, 15:40-17:40 , Room E
Blind Source Separation and Blind Information Processing: Promising Tools for Analysis of Multi-channel and Multidimensional Data
2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan |
Abstract
Blind signal processing (BSP) methods such as independent component analysis (ICA), nonnegative matrix factorization (NMF), Multi-channel Morphological Component Analysis (MMCA) or sparse component analysis (SCA) refers to wide class of problems in signal and image processing, when one needs to extract the underlying sources, hidden (latent) components or features from a set of mixtures or complex redundant observed data and information about system and inputs is limited. The goal of BSP can be considered as estimation of true physical sources and parameters of a mixing system, while objective of a generalized component analysis (GCA) is finding a new reduced or hierarchical and structured representation for the observed (sensor) multidimensional data that can be interpreted as physically meaningful coding or blind signal decompositions. These methods are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. The recent trends in blind source separation (BSS) and the generalized component analysis (GCA) is to consider problems in the framework of matrix factorization or more general signals decomposition with probabilistic generative and tree structured graphical models and exploit some priori knowledge about true nature and structure of latent (hidden) components or sources such as statistical independence, sparseness, non-negativity, smoothness or lowest possible complexity. The key issue is to find a such transformation or sparse coding which has true physical meaning and interpretation. In this tutorial we overview and discuss some promising approaches and algorithms for BSP, especially for ICA, NMF and SCA in order to estimate unknown sources signals, perform feature extraction, dimension reduction and object recognition, remove artifacts and denoising of multi-modal, multi-sensory data. Application to Brain Machine Interface (BMI) will be also briefly presented.
Keywords:
Independent Component Analysis (ICA) and its extensions. Sparse Component Analysis (SCA), Nonnegative Matrix Factorization (NMF), Multichannel Blind Deconvolution and their applications, Computer simulations and software: ICALAB, NMFLAB.
Biosketch
Andrzej Cichocki received the M.Sc. (with honors), Ph.D. and Dr.Sc. (Habilitation) degrees, all in electrical engineering. from Warsaw University of Technology (Poland). Since 1972, he has been with the Institute of Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering at the Warsaw University of Technology, where he obtain a title of a full Professor in 1995. He spent several years at University Erlangen-Nuerenberg (Germany), at the Chair of Applied and Theoretical Electrical Engineering directed by Professor Rolf Unbehauen, as an Alexander-von-Humboldt Research Fellow and Guest Professor. In 1995-1997 he was a team leader of the laboratory for Artificial Brain Systems, at Frontier Research Program RIKEN (Japan), in the Brain Information Processing Group. He is currently the head of the laboratory for Advanced Brain Signal Processing, at RIKEN Brain Science Institute (JAPAN) in the Brain-Style Computing Group directed by Professor Shun-ichi Amari.
He is co-author of more than 200 technical papers and three internationally recognized monographs (two of them translated to Chinese): Adaptive Blind Signal and Image Processing (Wiley, April 2003 -revised edition) , CMOS Switched-Capacitor and Continuous-Time Integrated Circuits and Systems (Springer-Verlag, 1989) and Neural Networks for Optimizations and Signal Processing (Teubner-Wiley,1994). He is Editor in Chief of International Journal Computational Intelligence and Neuroscience and Associate Editor of IEEE Transactions on Neural Networks.
