CPM: A covariance-preserving projection method

Jieping Ye, Tao Xiong, Ravi Janardan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Dimension reduction is critical in many areas of data mining and machine learning. In this paper, a Covariance-preserving Projection Method (CPM for short) is proposed for dimension reduction. CPM maximizes the class discrimination and also preserves approximately the class covariance. The optimization involved in CPM can be formulated as low rank approximations of a collection of matrices, which can be solved iteratively. Our theoretical and empirical analysis reveals the relationship between CPM and Linear Discriminant Analysis (LDA), Sliced Average Variance Estimator (SAVE), and Heteroscedastic Discriminant Analysis (HDA). This gives us new insights into the nature of these different algorithms. We use both synthetic and real-world datasets to evaluate the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the Sixth SIAM International Conference on Data Mining
PublisherSociety for Industrial and Applied Mathematics
Number of pages11
ISBN (Print)089871611X, 9780898716115
StatePublished - 2006
EventSixth SIAM International Conference on Data Mining - Bethesda, MD, United States
Duration: Apr 20 2006Apr 22 2006

Publication series

NameProceedings of the Sixth SIAM International Conference on Data Mining


OtherSixth SIAM International Conference on Data Mining
Country/TerritoryUnited States
CityBethesda, MD


  • Covariance
  • Dimension reduction
  • Heteroscedastic discriminant analysis
  • Linear discriminant analysis

ASJC Scopus subject areas

  • Engineering(all)


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