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Indexed by:期刊论文

Date of Publication:2014-01-01

Journal:ABSTRACT AND APPLIED ANALYSIS

Included Journals:SCIE

ISSN No.:1085-3375

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.