| Riemannian-Gradient-Based Learning on the Complex Matrix-Hypersphere. | |
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MedLine Citation:
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PMID: 21984497 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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This brief tackles the problem of learning over the complex-valued matrix-hypersphere Sɑn, p(C). The developed learning theory is formulated in terms of Riemannian-gradient-based optimization of a regular criterion function and is implemented by a geodesic-stepping method. The stepping method is equipped with a geodesic-search sub-algorithm to compute the optimal learning stepsize at any step. Numerical results show the effectiveness of the developed learning method and of its implementation. |
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Authors:
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Simone Fiori |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-10-06 |
Journal Detail:
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Title: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council Volume: - ISSN: 1941-0093 ISO Abbreviation: - Publication Date: 2011 Oct |
Date Detail:
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Created Date: 2011-10-10 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101211035 Medline TA: IEEE Trans Neural Netw Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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