PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

From learning metrics towards dependency exploration
Samuel Kaski
In: WSOM'05, 5th Workshop On Self-Organizing Maps, 5-8 Sep 2005, Paris, France.

Abstract

We have recently introduced new kinds of data fusion techniques, where the goal is to find what is shared by data sets, instead of modeling all variation in data. They extend our earlier works on learning of distance metrics, discriminative clustering, and other supervised statistical data mining methods. In the new methods the supervision is symmetric, which translates to mining of dependencies. We have so far introduced methods for associative clustering and for extracting dependent components which generalize classical canonical correlations.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:1695
Deposited By:Samuel Kaski
Deposited On:28 November 2005