Two ICA Papers Available

[Dr. Mark Girolami]

Dear Colleagues,
The following papers are available in gzipped postscript form at the
following website

http://www-cis.paisley.ac.uk/scripts/staff.pl//giro-ci0/index.html

Many Thanks
Best Rgds
Mark Girolami
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Title: An Alternative Perspective on Adaptive Independent Component
Analysis Algorithms

Author: Mark Girolami
Publication: Neural Computation, Vol.10, No.8, pp 2103-2114, 1998.

Abstract:
This paper develops an extended independent component analysis algorithm
for mixtures of arbitrary sub-Gaussian and super-Gaussian sources. The
Gaussian mixture model of Pearson is employed in deriving a closed-form
generic score function for strictly sub-Gaussian sources. This is
combined with the score function for a uni-modal super-Gaussian density
to provide a computationally simple yet powerful algorithm for
performing independent component analysis on arbitrary mixtures of
non-Gaussian sources. 

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Title: A Common Neural Network Model for Unsupervised Exploratory 
Data Analysis and Independent Component Analysis

Authors: Mark Girolami, Andrzej Cichocki, and Shun-Ichi Amari
Publication: I.E.E.E Transactions on Neural Networks, To Appear.

Abstract:
This paper presents the derivation of an unsupervised learning
algorithm, which enables the identification and visualisation of latent
structure within ensembles of high dimensional data. This provides a
linear projection of the data onto a lower dimensional subspace to
identify the characteristic structure of the observations independent
latent causes. The algorithm is shown to be a very promising tool for
unsupervised exploratory data analysis and data visualisation.
Experimental results confirm the attractiveness of this technique for
exploratory data analysis and an empirical comparison is made with the
recently proposed Generative Topographic Mapping (GTM) and standard
principal component analysis (PCA). Based on standard probability
density models a generic nonlinearity is developed which allows both; 1)
identification and visualisation of dichotomised clusters inherent in
the observed data and, 2) separation of sources with arbitrary
distributions from mixtures, whose dimensionality may be greater than
that of number of sources. The resulting algorithm is therefore also a
generalised neural approach to independent component analysis (ICA) 
and it is considered to be a promising method for analysis of real world
data that will consist of sub and super-Gaussian components such as
biomedical signals. 
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Dr. Mark Girolami (TM)
RIKEN, Brain Science Institute
Laboratory for Open Information Systems
2-1 Hirosawa, Wako-shi, Saitama 351-01, Japan
Email:  giro at open.brain.riken.go.jp
Tel:    +81 48 467 9666
Tel:    +81 48 462 3769 (apartment)
Fax:    +81 48 467 9694
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Currently on Secondment From:

Department of Computing and Information Systems
University of Paisley
High Street, PA1 2BE
Scotland, UK
Email: giro0ci at paisley.ac.uk
Tel: +44 141 848 3963
Fax: +44 141 848 3542

Secretary: Mrs E Campbell
Tel: +44 141 848 3966
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