An Effective Clustering Optimization Method for Unsupervised Linear Discriminant Analysis

Quan Wang, Fei Wang, Fuji Ren, Zhongheng Li, Feiping Nie

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The recent work Unsupervised Linear Discriminant Analysis (Un-LDA) completes its clustering process during the alternating optimization by converting equivalently the objective and finally using the K-means algorithm. However, the K-means algorithm has its inherent drawbacks. It is hard for the K-means algorithm to deal well with some complex clustering cases where there are too many real clusters or non-convex clusters. In this paper, a novel clustering optimization method is presented to accomplish the clustering process in Un-LDA and the resulting method can be named Un-LDA(CD). Specifically, instead of the K-means algorithm, an elaborately designed coordinate descent algorithm is adopted to obtain the clusters after the objective function goes through a series of simple but deft equivalent conversions. Extensive experiments have demonstrated that the coordinate descent clustering solution for Un-LDA can outperform the original K-means based solution on the tested data sets especially those complex data sets with a pretty large number of real clusters.

Original languageEnglish
Pages (from-to)3444-3457
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume35
Issue number4
DOIs
StatePublished - 1 Apr 2023

Keywords

  • Clustering optimization method
  • coordinate descent
  • dimensionality reduction
  • linear discriminant analysis
  • unsupervised learning

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