Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-20
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Xu, Min& Fang, Qin& Wang, Shaofan. 2014. Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1013461
Modern Language Association (MLA)
Xu, Min…[et al.]. Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity. Abstract and Applied Analysis No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1013461
American Medical Association (AMA)
Xu, Min& Fang, Qin& Wang, Shaofan. Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1013461
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1013461