Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity
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 particul...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/197476 |
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| _version_ | 1850222721099628544 |
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| author | Min Xu Qin Fang Shaofan Wang |
| author_facet | Min Xu Qin Fang Shaofan Wang |
| author_sort | Min Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-700b62aca8074e72aeefc0ce25bdce75 |
| institution | OA Journals |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-700b62aca8074e72aeefc0ce25bdce752025-08-20T02:06:15ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/197476197476Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated SparsityMin Xu0Qin Fang1Shaofan Wang2School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, ChinaCollege of Information, Dalian University, Dalian 116622, ChinaBeijing Key Laboratory of Multimedia Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, ChinaWe 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.http://dx.doi.org/10.1155/2014/197476 |
| spellingShingle | Min Xu Qin Fang Shaofan Wang Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity Abstract and Applied Analysis |
| title | Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity |
| title_full | Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity |
| title_fullStr | Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity |
| title_full_unstemmed | Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity |
| title_short | Convergence Analysis of an Empirical Eigenfunction-Based Ranking Algorithm with Truncated Sparsity |
| title_sort | convergence analysis of an empirical eigenfunction based ranking algorithm with truncated sparsity |
| url | http://dx.doi.org/10.1155/2014/197476 |
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