PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernelized Sorting
N. Quadrianto, A.J. Smola, L. Song and Tinne Tuytelaars
IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 32, Number 10, pp. 1809-1821, 2010.

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Information Retrieval & Textual Information Access
ID Code:9287
Deposited By:Tinne Tuytelaars
Deposited On:21 February 2012