Making every bit count

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Making every bit count: fast nonlinear axis scaling

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Edmonton, Alberta, Canada

POSTER SESSION: Poster papers table of contents

Pages: 664 - 669

Year of Publication: 2002

ISBN:1-58113-567-X

Authors

Leejay Wu	Carnegie Mellon University
Christos Faloutsos	Carnegie Mellon University

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
: AAAI

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 2, Downloads (12 Months): 18, Citation Count: 1

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ABSTRACT

Existing axis scaling and dimensionality methods focus on preserving structure, usually determined via the Euclidean distance. In other words, they inherently assume that the Euclidean distance is already correct. We instead propose a novel nonlinear approach driven by an information-theoretic viewpoint, which we show is also strongly linked to intrinsic dimensionality, or degrees of freedom; and uniformity. Nonlinear transformations based on common probability distributions, combined with information-driven selection, simultaneously reduce the number of dimensions required and increase the value of those we retain. Experiments on real data confirm that this approach reveals correlations, finds novel attributes, and scales well.

REFERENCES

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