PROCEEDINGS IPMU '08

Fuzzy skeletons and statistical learning theory

Pando Georgiev, Anca Ralescu.

We define a fuzzy subspace skeleton of
data points and propose an algorithm
for finding it. Such a skeleton has direct
applications in statistical learning
theory. We propose a new type of classifiers:
fuzzy skeleton classifiers, which
might be a better alternative to Support
Vector Machines in some cases. Another
application is presented to the unsupervised
learning - Blind Signal Separation,
based on mild sparsity assumptions.
Our methods are illustrated by
examples. Potential application include
problems from bioinformatics as separation
of protein spectra, gene expressions,
etc., as well as any problems requiring
signal separation in which Independent
Component Analysis doesn’t
work, or gives unsatisfactory results.

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