| Semisupervised kernel matrix learning by kernel propagation. | |
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MedLine Citation:
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PMID: 20923733 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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The goal of semisupervised kernel matrix learning (SS-KML) is to learn a kernel matrix on all the given samples on which just a little supervised information, such as class label or pairwise constraint, is provided. Despite extensive research, the performance of SS-KML still leaves some space for improvement in terms of effectiveness and efficiency. For example, a recent pairwise constraints propagation (PCP) algorithm has formulated SS-KML into a semidefinite programming (SDP) problem, but its computation is very expensive, which undoubtedly restricts PCPs scalability in practice. In this paper, a novel algorithm, called kernel propagation (KP), is proposed to improve the comprehensive performance in SS-KML. The main idea of KP is first to learn a small-sized sub-kernel matrix (named seed-kernel matrix) and then propagate it into a larger-sized full-kernel matrix. Specifically, the implementation of KP consists of three stages: 1) separate the supervised sample (sub)set X(l) from the full sample set X; 2) learn a seed-kernel matrix on X(l) through solving a small-scale SDP problem; and 3) propagate the learnt seed-kernel matrix into a full-kernel matrix on X . Furthermore, following the idea in KP, we naturally develop two conveniently realizable out-of-sample extensions for KML: one is batch-style extension, and the other is online-style extension. The experiments demonstrate that KP is encouraging in both effectiveness and efficiency compared with three state-of-the-art algorithms and its related out-of-sample extensions are promising too. |
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Authors:
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Enliang Hu; Songcan Chen; Daoqiang Zhang; Xuesong Yin |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2010-10-04 |
Journal Detail:
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Title: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council Volume: 21 ISSN: 1941-0093 ISO Abbreviation: IEEE Trans Neural Netw Publication Date: 2010 Nov |
Date Detail:
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Created Date: 2010-11-04 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101211035 Medline TA: IEEE Trans Neural Netw Country: United States |
Other Details:
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Languages: eng Pagination: 1831-41 Citation Subset: IM |
Affiliation:
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Department of Mathematics, Yunnan Normal University, Kunming, China. helnuaa@nuaa.edu.cn |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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