Independent Component Analysis (ICA) is emerging as a new standard area of signal processing and data analysis. ICA attempts to solve the blind source separation problem in which sensor signals are unknown mixtures of unknown source signals. While there are no general analytical solutions, in the last decade researchers have proposed good approximate methods based on simple assumptions about the source statistics and using maximum likelihood, information maximization and minimization of higher-order moments.
ICA theory has received attention from several research communities including machine learning, neural networks, statistical signal processing and Bayesian modeling. More recently numerous applications of ICA have appeared including applications to adaptive speech filtering, speech signal coding, biomedical signal processing, image compression, text modeling and financial data analysis. ICA2001 will feature the latest developments in the new field of blind source separation. The Workshop will feature internationally respected keynote speakers, poster sessions, and symposia on theory, on algorithms and on applications to a wide range of fields and data types. The Conference recreational program includes an informal banquet and a unique opening cocktail party / unmixer.
This is the third international meeting in this series. The previous two meetings were held in Aussois, France (December, 1999) and Helsinki, Finland (June, 2000). This year's event will be held December 9-12, 2001 immediately following the Neural Information Processing Systems (NIPS) conference in Vancouver, Canada and its post-conference workshops. The invited speakers for ICA2001 are: Andrew Viterbi (Qualcomm), Michael Jordan (UC Berkeley), Robert Hecht-Nielsen (HNC & UCSD), Mohan Trivedi (UCSD), Cristoph Koch (Caltech), Bhaskar Rao (UCSD), Giles Laurent (Caltech), Geoffry Hinton (U. of Toronto), and Tony Bell (The Salk Institute).