Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models

2017/10/30 by Aoshima, Makoto, Yata, Kazuyoshi · 1 citation
#FOS: Computer and information sciences #Machine Learning (stat.ML) #Primary 62H30 #secondary 62H25

paper · doi:10.48550/arxiv.1710.10768

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.

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