Release Time:2019-03-09  Hits:

Indexed by: Journal Article

Date of Publication: 2014-01-01

Journal: ABSTRACT AND APPLIED ANALYSIS

Included Journals: SCIE

ISSN: 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.