Abstract
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.
Original language | English |
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Article number | 5342424 |
Pages (from-to) | 1809-1821 |
Number of pages | 13 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 32 |
Issue number | 10 |
DOIs | |
Publication status | Published - 2010 |