On fused dimension reduction in multivariate regression

Keunbaik Lee, Yuri Choi, Hye Yeon Um, Jae Keun Yoo

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


High-dimensional data analysis often suffers the so-called curse of dimensionality, and various data reduction methods are adopted in order to avoid it in practice. Consequently, in multivariate regression, high-dimensional predictors should be reduced to lower-dimensional ones without the loss of information, following a notion of sufficient dimension reduction. In this paper, a fused clustered seeded reduction approach is proposed for multivariate regression. The proposed method utilizes two types of information: supervised learning between the responses and the predictors, and unsupervised learning of the predictors alone. Fusing all the information has a potential advantage in the accuracy of the reduction of predictors. Numerical studies and a real data analysis confirm the practical usefulness of the proposed approach over existing methods.

Original languageEnglish
Article number103828
JournalChemometrics and Intelligent Laboratory Systems
StatePublished - 15 Oct 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier B.V.


  • Fused approach
  • K-means clustering
  • Large p small n
  • Multivariate analysis
  • Seeded reduction


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