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

TL;DR

This paper introduces a novel distance-based classifier for high-dimensional data with strongly spiked eigenvalues, utilizing data transformation and noise reduction to improve classification accuracy, demonstrated through simulations and microarray data.

Contribution

The paper proposes a new classifier that transforms high-dimensional data from the SSE model to a non-SSE model, enhancing classification performance.

Findings

Effective noise reduction in eigenstructure estimation

Improved classification accuracy in simulations

Successful application to microarray datasets

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.