Combining Variable Selection with Dimensionality Reduction

Combining Variable Selection with Dimensionality Reduction

This paper bridges the gap between variable selection methods (e.g., Pearson coefficients, KS test) and dimensionality reductionalgorithms (e.g., PCA, LDA). Variable selection algorithms encounter difficulties dealing with highly correlated data,since many features are similar in quality. Dimensiona...

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Bibliographic Details
Main Authors: Wolf, Lior, Bileschi, Stanley
Language:en_US
Published: 2005
Subjects:
AI
Computer Vision
Statistical Learning
Variable Selection
Online Access:http://hdl.handle.net/1721.1/30531

Internet

http://hdl.handle.net/1721.1/30531

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