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

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)1561-1577
Number of pages17
JournalCommunications in Statistics - Theory and Methods
Volume49
Issue number7
DOIs
Publication statusPublished - 2 Apr 2020

Keywords

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

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