| Trace ratio problem revisited. | |
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MedLine Citation:
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PMID: 19304481 Owner: NLM Status: PubMed-not-MEDLINE |
Abstract/OtherAbstract:
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Dimensionality reduction is an important issue in many machine learning and pattern recognition applications, and the trace ratio (TR) problem is an optimization problem involved in many dimensionality reduction algorithms. Conventionally, the solution is approximated via generalized eigenvalue decomposition due to the difficulty of the original problem. However, prior works have indicated that it is more reasonable to solve it directly than via the conventional way. In this brief, we propose a theoretical overview of the global optimum solution to the TR problem via the equivalent trace difference problem. Eigenvalue perturbation theory is introduced to derive an efficient algorithm based on the Newton-Raphson method. Theoretical issues on the convergence and efficiency of our algorithm compared with prior literature are proposed, and are further supported by extensive empirical results. |
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Authors:
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Yangqing Jia; Feiping Nie; Changshui Zhang |
Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2009-03-16 |
Journal Detail:
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Title: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council Volume: 20 ISSN: 1941-0093 ISO Abbreviation: - Publication Date: 2009 Apr |
Date Detail:
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Created Date: 2009-04-03 Completed Date: 2009-06-18 Revised Date: 2009-10-28 |
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: 729-35 Citation Subset: - |
Affiliation:
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Department of Automation, State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology Tsinghua University, Beijing, China. |
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Descriptor/Qualifier:
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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