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DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA

Tian:DIMENSIONALITY REDUCTION FOR CLASS

Autor:	Siva Tian
Verfügbarkeit:	Auf Lager.
Artikelnummer:	1312713
ISBN / EAN:	9783639288681

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DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA

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Autor: Siva Tian
Verlag: VDM Verlag Dr. Müller
ISBN / EAN: 9783639288681
Bindung: Taschenbuch

Produktbeschreibung

High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies.