JMLR special issue on ICA

[Te-Won Lee]

 Journal of Machine Learning Research

 Special Issue on "Independent Component Analysis"

 Guest Editors:
      Te-Won Lee, Jean-Francois Cardoso, Erkki Oja, Shun-Ichi Amari


      CALL FOR PAPERS

      We  invite papers  on Independent  Component Analysis  (ICA) and
      Blind Source Separation (BSS) for a special issue in the Journal
      of Machine Learning Research (on-line publication and subsequent
      publication from MIT Press).

      In recent  years, ICA has received attention  from many research
      areas including statistical signal processing, machine learning,
      neural  networks,   information  theory  and   exploratory  data
      analysis.  Applications  of  ICA  algorithms  in  speech  signal
      processing  and  biomedical signal  processing  are growing  and
      maturing  and ICA  methods  are also  considered  in many  other
      fields  where this  novel data  analysis technique  provides new
      insights.

      Recent  approaches to  ICA such  as variational  methods, kernel
      methods  and  tensor  methods   have  lead  to  new  theoretical
      insights. They  permit us to relax  some of the constraints  in the
      traditional   ICA  assumptions   yielding  new   algorithms  and
      increasing the domains  of application. Certain nonlinear mixing
      systems can be inverted, more  sources than the number of sensors
      can be  recovered, and  further understanding of  the convergence
      properties and gradient optimizations are now available.

      The  ICA framework  is an  interdisciplinary research  area. The
      combination  of  ideas  from  machine learning  and  statistical
      signal processing is a developing  avenue of research and ICA is
      a first step into this new direction.

      We  invite original contributions  that explore  theoretical and
      practical issues related to ICA.


      A list of possible topics include:

        Theory and Algorithms
            Bayesian methods
            Information theoretic approaches 
            High order statistics
            Convolutive mixtures
            Convergence and stability issues
            Graphical models
            Nonlinear mixing
            Undercomplete mixtures
            Sparse coding

        Methodology and Applications
            Biomedical applications
            Speech signal processing
            Image processing
            Performance comparisons 
            Model validation
            Dimension reduction and visualization 
            Learning features in high dimensional data

          
      Important Dates:
      - Submission:     October, 1st 2002
      - Decision:       January, 1st 2003
      - Final:            March, 1st 2003


      Submission procedure:  see http://rhythm.ucsd.edu/~tewon/JMLR.html

      For further details or enquiries, send mail to tewon at inc.ucsd.edu

      Links: 
            http://www-sig.enst.fr/~ica99/
            http://www.cis.hut.fi/ica2000
            http://www.ica2001.org
            http://ica2003.jp