On fused dimension reduction in multivariate regression
- 주제(키워드) Fused approach , K-means clustering , Large p small n , Multivariate analysis , Seeded reduction
- 등재 SCIE, SCOPUS
- 발행기관 Elsevier B.V.
- 발행년도 2019
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000162281
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.chemolab.2019.103828
초록/요약
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. © 2019 Elsevier B.V.
more