Kernelized Sorting

  • Authors:

  • Novi Quadrianto;Alexander J. Smola;Le Song;Tinne Tuytelaars

  • Affiliations:

  • Australian National University and NICTA, Canberra;Yahoo! Research, Santa Clara;Carnegie Mellon University, Pittsburgh;K.U. Leuven ESAT-PSI, Leuven

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 2010

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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.