Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity

Min Xu, Qin Fang and Shaofan Wang

Abstract and Applied Analysis, 2014, vol. 2014, 1-8

Abstract:

We study an empirical eigenfunction-based algorithm for ranking with a data dependent hypothesis space. The space is spanned by certain empirical eigenfunctions which we select by using a truncated parameter. We establish the representer theorem and convergence analysis of the algorithm. In particular, we show that under a mild condition, the algorithm produces a satisfactory convergence rate as well as sparse representations with respect to the empirical eigenfunctions.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaaa:197476

DOI: 10.1155/2014/197476

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