title: Kernelized sorting.
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.
authors: RSISE, Australian National University and the Statistical Machine Learning Group, Canberra Research Laboratory, NICTA, Tower A, 7 London Circuit, Canberra City, ACT 2601, Australia. novi.quad@gmail.com
authors: Novi
authors: N
authors: Quadrianto
authors: Alex J
authors: AJ
authors: Smola
authors: Le
authors: L
authors: Song
authors: Tinne
authors: T
authors: Tuytelaars