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|a eng
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|a Lebanon, Guy
|e verfasserin
|4 aut
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|a Metric learning for text documents
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|c 2006
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|a Text
|b txt
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|a ohne Hilfsmittel zu benutzen
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|a Date Completed 18.04.2006
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|a Date Revised 01.12.2018
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|a published: Print
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|a Citation Status MEDLINE
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|a 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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|a Journal Article
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1998
|g 28(2006), 4 vom: 11. Apr., Seite 497-508
|w (DE-627)NLM098212257
|x 0162-8828
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|g volume:28
|g year:2006
|g number:4
|g day:11
|g month:04
|g pages:497-508
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