Quadrianto, N and Song, L and Smola, AJ (2009) Kernelized sorting. Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. pp. 1289-1296.Full text not available from this repository.
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.
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:52|
|Last Modified:||19 Feb 2017 00:05|