Sparse optimal discriminant clustering

Yanhong Wang, Yixin Fang, Junhui Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this manuscript, we reinvestigate an existing clustering procedure, optimal discriminant clustering (ODC; Zhang and Dai in Adv Neural Inf Process Syst 23(12):2241–2249, 2009), and propose to use cross-validation to select the tuning parameter. Furthermore, because in high-dimensional data many of the features may be non-informative for clustering, we develop a variation of ODC, sparse optimal discriminant clustering (SODC), by adding a group-lasso type of penalty to ODC. We also demonstrate that both ODC and SDOC can be used as a dimension reduction tool for data visualization in cluster analysis.

Original languageEnglish (US)
Pages (from-to)629-639
Number of pages11
JournalStatistics and Computing
Volume26
Issue number3
DOIs
StatePublished - May 1 2016

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

Keywords

  • Cluster analysis
  • Cross-validation
  • High-dimensional data
  • Optimal score
  • Principal components analysis
  • Tuning parameter
  • Variable selection

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