Title: Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models,
Abstract: 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.