Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models
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We consider classifiers for high-dimensional data under the strongly spiked eigenvalue (SSE) model. We first show that high-dimensional data often have the SSE model. We consider a distance-based classifier using eigenstructures for the SSE model. We apply the noise-reduction methodology to estimation of the eigenvalues and eigenvectors in the SSE model. We create a new distance-based classifier by transforming data from the SSE model to the non-SSE model. We give simulation studies and discuss the performance of the new classifier. Finally, we demonstrate the new classifier by using microarray data sets.
KeywordsAsymptotic normality Data transformation Discriminant analysis Large p small n Noise-reduction methodology Spiked model
We would like to thank two anonymous referees for their constructive comments.
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