skip to content
 

Dimension selection with independent component analysis and its application to prediction

Date: 
Friday 27th June 2008 - 09:20 to 09:40
Venue: 
INI Seminar Room 1
Session Chair: 
Mike Titterington
Abstract: 

We consider the problem of selecting the best or most informative dimension for dimension reduction and feature extraction in high-dimensional data. We review current methods, and propose a dimension selector based on Independent Component Analysis which finds the most non-Gaussian lower-dimensional directions in the data. A criterion for choosing the optimal dimension is based on bias-adjusted skewness and kurtosis. We show how this dimension selector can be applied in supervised learning with independent components, both in a regression and classification framework.

The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons