Kernelized sorting

IEEE Trans Pattern Anal Mach Intell. 2010 Oct;32(10):1809-21. doi: 10.1109/TPAMI.2009.184.


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.

Publication types

  • Research Support, Non-U.S. Gov't