PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Ranking with kernels in Fourier space
Risi Kondor and M Barbosa
In: Computational Learning Theory (COLT), Haifa, Israel(2010).


In typical ranking problems the total number n of items to be ranked is relatively large, but each data instance involves only k << n items. This paper examines the structure of such partial rankings in Fourier space. Specically, we develop a kernel-based framework for solving ranking problems, define some canonical kernels on permutations, and show that by transforming to Fourier space, the complexity of computing the kernel between two partial rankings can be reduced from O((n-k)!^2) to O((2k) ^{2k+3}).

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:7332
Deposited By:Wray Buntine
Deposited On:17 March 2011