Software for ICA and BSS

[cia@hare.riken.go.jp]

Dear all,

Just to let you know of the http availability of a new software 
for Independent Component Analysis (ICA) and Blind Separation of
Sources (BSS).

The Laboratory for Open Information Systems in the Research Group of
Professor S. AMARI, (Brain-Style Information Processing Group) BRAIN
SCIENCE INSTITUTE -RIKEN, JAPAN announces the availability of OOLABSS
(Object Oriented LAboratory for Blind Source Separation), an
experimental laboratory for ICA and BSS.

OOLABSS has been developed by Dr. A. CICHOCKI and Dr. B. ORSIER (both
worked on the concept of this software and on the
development/unification of learning algorithms, while Dr. B. ORSIER
designed and implemented the software in C++ under Windows95/NT).

OOLABAS  offers an interactive environment for experiments
with a very wide family of recently developed on-line adaptive learning
algorithms for Blind Separation of Sources and Independent Component
Analysis.

OOLABSS is free for non-commercial use. The current version is still
experimental but is reasonably stable and robust.

The program has the following features:

1. Users can define their own activation functions for each neuron 
   (processing unit) or use a global activation function 
   (e.g. hyperbolic tangent) for all neurons.

2. The program also enables automatic (self-adaptive) selection of
   quasi optimal activation functions (time variable or switching) 
   depending on the stochastic distribution of extracted source signals 
   (so called extended ICA problem).

3  Users can add a noise both to sensors signals as well as to synaptic 
   weights.

4. The number of sources, sensors and outputs of the neural network can be
   arbitrary defined by users.	

5. In the case where the number of source signals is completely unknown one 
   of the proposed approaches enables not only to estimate source signals
   but also to estimate correctly their number on-line 
   without any pre-processing, like pre-whitening or Principal Component 
   Analysis (PCA).

6. The problem of optimal updating of a learning rate (step) is 
   a key problem encountered in a wide class of on-line adaptive 
   learning algorithms. Relying on properties of nonlinear low-pass 
   filters a family of learning algorithms for self-adaptive 
   (automatic) updating of learning rates (global one or 
   local-individual for each synaptic weight) are implemented 
   in the program. 
   The learning rates can be self-adaptive, i.e. quasi optimal
   annealing of learning rates is automatically provided in a stationary 
   environment. In a non-stationary environment the learning rates 
   adaptively change their value to provide good tracking abilities.
   The users can also define their own function for changing the 
   learning rate.

6. The program enables to compare performance of several different 
   algorithms.

7. Special emphasis is given to robust algorithms with respect to 
   noise and outliers and equivariant feature (i.e. independence of 
   asymptotic performance for ill conditioning of the mixing process). 
  
8. Advanced graphics: illustrative figures are produced and can be
   easily printed. Encapsulated Postscript files can be produced for
   easy integration into word processors. Data can be pasted to the
   clipboard for post-processing using specialized software like
   Matlab or even spreadsheets.

9. Users can easily enter their own data (sensors signals, or sources and 
   mixing matrix, noise, a neural network model, etc.) in order to 
   experiment with various kind of algorithms. 

10. Modular programming style: the program code is based on 
    well-defined C++ classes and is very modular, which makes it
    possible to tailor the software to each user's specific needs.

Please visit OOLABSS home page at URL:
http://www.bip.riken.go.jp/absl/orsier/OOLABSS

The version is 1.0 beta, so comments, suggestions and bug
reports are welcome at the address: oolabss at open.brain.riken.go.jp