| Metric learning for text documents. | |
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MedLine Citation:
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PMID: 16566500 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Many algorithms in machine learning rely on being given a good distance metric over the input space. Rather than using a default metric such as the Euclidean metric, it is desirable to obtain a metric based on the provided data. We consider the problem of learning a Riemannian metric associated with a given differentiable manifold and a set of points. Our approach to the problem involves choosing a metric from a parametric family that is based on maximizing the inverse volume of a given data set of points. From a statistical perspective, it is related to maximum likelihood under a model that assigns probabilities inversely proportional to the Riemannian volume element. We discuss in detail learning a metric on the multinomial simplex where the metric candidates are pull-back metrics of the Fisher information under a Lie group of transformations. When applied to text document classification the resulting geodesic distance resemble, but outperform, the tfidf cosine similarity measure. |
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Authors:
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Guy Lebanon |
Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: IEEE transactions on pattern analysis and machine intelligence Volume: 28 ISSN: 0162-8828 ISO Abbreviation: IEEE Trans Pattern Anal Mach Intell Publication Date: 2006 Apr |
Date Detail:
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Created Date: 2006-03-28 Completed Date: 2006-04-18 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9885960 Medline TA: IEEE Trans Pattern Anal Mach Intell Country: United States |
Other Details:
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Languages: eng Pagination: 497-508 Citation Subset: IM |
Affiliation:
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Department of Statistics, Purdue University, 150 N. University Street, West Lafayette, IN 47907, USA. lebanon@stat.purdue.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Artificial Intelligence* Automatic Data Processing / methods* Computer Graphics Documentation / methods* Image Enhancement / methods Image Interpretation, Computer-Assisted / methods* Information Storage and Retrieval / methods* Numerical Analysis, Computer-Assisted Pattern Recognition, Automated / methods* Signal Processing, Computer-Assisted User-Computer Interface |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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