New papers on ICA

[Aapo Hyvarinen]

Dear All,

The following papers on ICA and related topics are now available on my
home page: http://www.cis.hut.fi/aapo/pub.html

I would also like to mention that I'm going to give a tutorial on ICA at
the IJCNN'00 in Como, Italy.

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A. Hyvarinen.
Complexity Pursuit: Separating interesting components from time-series.

Shorter version appeared in Proc. Int. Workshop on Independent
Component Analysis and Blind Signal Separation (ICA2000),
Helsinki, Finland, 2000  
http://www.cis.hut.fi/aapo/ps/gz/complexity.ps.gz

Abstract: A generalization of projection pursuit for time series,
i.e. signals with time structure, is introduced.  The goal is to find
projections of time series that have interesting structure. We define
the interestingness using criteria related to Kolmogoroff Complexity
or coding length: Interesting signals are those that can be coded with
a short code length.  We derive a simple approximation of coding
length that takes into account both the nongaussianity and the
autocorrelations of the time series. Also, we derive a simple
algorithm for its approximative optimization.  The resulting method is
closely related to blind separation of nongaussian, time-dependent
source signals.

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P.O. Hoyer and A. Hyvarinen.
Independent Component Analysis Applied to Feature Extraction from
Colour and Stereo Images.  

To appear in Network.
http://www.cis.hut.fi/aapo/ps/gz/Network00.ps.gz

Abstract: Previous work has shown that independent component analysis
(ICA) applied to feature extraction from natural image data yields
features resembling Gabor functions and simple-cell receptive
fields. This article considers the effects of including chromatic and
stereo information. The inclusion of colour leads to features divided
into separate red/green, blue/yellow, and bright/dark channels. Stereo
image data, on the other hand, leads to binocular receptive fields
which are tuned to various disparities. The similarities between these
results and observed properties of simple cells in primary visual
cortex are further evidence for the hypothesis that visual cortical
neurons perform some type of redundancy reduction, which was one of
the original motivations for ICA in the first place. In addition, ICA
provides a principled method for feature extraction from colour and
stereo images; such features could be used in image processing
operations such as denoising and compression, as well as in pattern
recognition.



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A. Hyvarinen and R. Karthikesh. 
Sparse priors on the mixing matrix in independent component analysis.

Proc. ICA2000, Helsinki, Finland.
http://www.cis.hut.fi/aapo/ps/gz/ICA00_sp.ps.gz

Abstract: In independent component analysis, prior information on the
distributions of the independent components is often used; some weak
information is in fact necessary for succesful estimation.  In
contrast, prior information on the mixing matrix is usually not
used. This is because it is considered that the estimation should be
completely blind as to the form of the mixing matrix.  Nevertheless,
it could be possible to find forms of prior information that are
sufficiently general to be useful in a wide range of applications. In
this paper, we argue that prior information on the sparsity of the
mixing matrix could be a constraint general enough to merit
attention. Moreover, we show that the computational implementation of
such sparsifying priors on the mixing matrix is very simple since in
many cases they can be expressed as conjugate priors. The property of
being conjugate priors means that essentially the same algorithm can
be used as in ordinary ICA.



Best Regards,

Aapo

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Aapo Hyvarinen

Neural Networks Research Centre
Helsinki University of Technology
P.O.Box 5400, FIN-02015 HUT, Finland
Tel: +358-9-4513278, Fax: +358-9-4513277

Email: Aapo.Hyvarinen at hut.fi
Home page: http://www.cis.hut.fi/~aapo/
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