A classifier under the strongly spiked eigenvalue model in high-dimension, low-sample-size context

Abstract

We consider the classification of high-dimensional data under the strongly spiked eigenvalue (SSE) model. We create a new classification procedure on the basis of the high-dimensional eigenstructure in high-dimension, low-sample-size context. We propose a distance-based classification procedure by using a data transformation. We also prove that our proposed classification procedure has consistency property for misclassification rates. We discuss performances of our classification procedure in simulations and real data analyses using microarray data sets.

Keywords:

• Data transformation
• HDLSS
• Large p
• small n
• Noise-reduction methodology
• SSE model

Mathematics Subject Classification:

• primary 62H30
• secondary 62H25

Acknowledgments

The author would like to thank the editor and the anonymous referee for their variable comments. Research of the author was partially supported by Grant-in-Aid for Young Scientists, Japan Society for the Promotion of Science (JSPS), under Contract Number 18K18015.