CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2010 vol.32 Issue No.10 - October
Issue No.10 - October (2010 vol.32)
Novi Quadrianto , Australian National University and NICTA, Canberra
Alex J. Smola , Yahoo! Research, Santa Clara
Le Song , Carnegie Mellon University, Pittsburgh
Tinne Tuytelaars , K.U. Leuven ESAT-PSI, Leuven
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.
Sorting, matching, kernels, object alignment, Hilbert-Schmidt Independence Criterion.
Novi Quadrianto, Alex J. Smola, Le Song, Tinne Tuytelaars, "Kernelized Sorting", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 10, pp. 1809-1821, October 2010, doi:10.1109/TPAMI.2009.184