| Multiplicative updates for nonnegative quadratic programming. | |
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MedLine Citation:
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PMID: 17571937 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Many problems in neural computation and statistical learning involve optimizations with nonnegativity constraints. In this article, we study convex problems in quadratic programming where the optimization is confined to an axis-aligned region in the nonnegative orthant. For these problems, we derive multiplicative updates that improve the value of the objective function at each iteration and converge monotonically to the global minimum. The updates have a simple closed form and do not involve any heuristics or free parameters that must be tuned to ensure convergence. Despite their simplicity, they differ strikingly in form from other multiplicative updates used in machine learning. We provide complete proofs of convergence for these updates and describe their application to problems in signal processing and pattern recognition. |
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Authors:
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Fei Sha; Yuanqing Lin; Lawrence K Saul; Daniel D Lee |
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Publication Detail:
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Type: Journal Article; Research Support, U.S. Gov't, Non-P.H.S. |
Journal Detail:
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Title: Neural computation Volume: 19 ISSN: 0899-7667 ISO Abbreviation: Neural Comput Publication Date: 2007 Aug |
Date Detail:
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Created Date: 2007-06-18 Completed Date: 2007-09-07 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9426182 Medline TA: Neural Comput Country: United States |
Other Details:
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Languages: eng Pagination: 2004-31 Citation Subset: IM |
Affiliation:
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Computer Science Division, University of California, Berkeley, Berkeley, CA 94720, USA. feisha@cs.berkeley.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Animals Humans Information Storage and Retrieval / methods* Neural Networks (Computer)* Programming, Linear* Signal Processing, Computer-Assisted* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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