Kernelized sorting

Novi Quadrianto*, Alexander J. Smola, Le Song, Tinne Tuytelaars

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    45 Citations (Scopus)

    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.

    Original languageEnglish
    Article number5342424
    Pages (from-to)1809-1821
    Number of pages13
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Volume32
    Issue number10
    DOIs
    Publication statusPublished - 2010

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