Tianping Chen
Department of Mathematics, Fudan University, Shanghai, China

Abstract
Independent component analysis or blind source separation extracts independent signals from their linear mixtures without knowing their mixing coefficients.
Blind Identification (or blind source separation /independent component analysis) is an emerging research field in both theory and applications. It has been motivated by practical applications that involve multiple source signals and observation sensors and share a common objective, that is to separate source signals and estimate channel parameters without knowing the characteristics of the transmission channel.
In this tutorial, we introduce some basic aspects, including derivation of demixing algorithms and their dynamical behaviors analysis.
In many information processing systems, it is necessary to extract the main features or eliminate the noise inherent in complex, high-dimensional input data streams. One of the most general purpose feature extraction techniques is Principal and Minor Component Analysis (PCA and MCA).
In this tutorial, we focus on stability analysis for various PCA (MCA) algorithms.

Speaker
Tianping Chen is a professor of Department of Mathematics in Fudan University, Shanghai, China. He is a recipient of several important awards, including second prize of the National Natural Sciences award of China in 2002, the outstanding paper award of IEEE Transactions on Neural Networks in 1997, the best paper award of Japanese Neural Network Society in 1997. His research interests include hamornic analysis, approximation theory, neural networks, signal processing and dynamical systems.