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...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Pattern Analysis and Machine Intelligence|
|01_Quadrianto_Kernelized_sorting_2010.pdf||327.83 kB||Adobe PDF||Request a copy|
|02_Quadrianto_Kernelized_sorting_2010.pdf||4.93 MB||Adobe PDF||Request a copy|
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