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

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Authors Makoto Aoshima, Kazuyoshi Yata
Journal/Conference Name Annals of the Institute of Statistical Mathematics
Paper Category
Paper 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.
Date of publication 2017
Code Programming Language Jupyter Notebook

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