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