PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernelized sorting
Novi Quadrianto, Le Song and Alex Smola
In: 22nd Annual Conference on Neural Information Processing Systems, 8-13 Dec 2008, Vancouver, B.C., Canada.


Object matching is a fundamental operation in data analysis. It typically requires the definition of a similarity measure between the classes of objects to be matched. Instead, we develop an approach which is able to perform matching by requiring a similarity measure only within each of the classes. This is achieved by maximizing the dependency between matched pairs of observations by means of the Hilbert Schmidt Independence Criterion. This problem can be cast as one of maximizing a quadratic assignment problem with special structure and we present a simple algorithm for finding a locally optimal solution.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5226
Deposited By:Novi Quadrianto
Deposited On:24 March 2009