Metric learning for text documents

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...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 28; no. 4; pp. 497 - 508
Main Author:	Lebanon, G.
Format:	Journal Article
Language:	English
Published:	Los Alamitos, CA IEEE 01.04.2006 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Applied sciences Artificial Intelligence Automatic Data Processing - methods Classification Computer Graphics Computer science; control theory; systems Distance learning Documentation - methods Euclidean distance Exact sciences and technology Geometry Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Information Storage and Retrieval - methods Inverse Joining processes Kernel Learning Level measurement Lie groups Machine learning Machine learning algorithms Neural networks Numerical Analysis, Computer-Assisted Pattern analysis Pattern Recognition, Automated - methods Probability Signal Processing, Computer-Assisted Similarity Speech and sound recognition and synthesis. Linguistics Studies Text analysis Texts Transformations User-Computer Interface Text analysis Statistical analysis Distance learning Probabilistic approach Metric space Similarity Euclidean theory Riemann metric Geodesic structure Text Transformation group machine learning Modeling Remote teaching Fisher information Classification Geodesic Lie group Maximum likelihood Lie transformation Learning algorithm Pattern analysis Artificial intelligence
ISSN:	0162-8828, 1939-3539
Online Access:	Get full text
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