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자연과학대학
통계학전공
Journal papers
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On fused dimension reduction in multivariate regression
Title
On fused dimension reduction in multivariate regression
Authors
Lee K.
;
Choi Y.
;
Um H.Y.
;
Yoo J.K.
Ewha Authors
유재근
SCOPUS Author ID
유재근
Issue Date
2019
Journal Title
Chemometrics and Intelligent Laboratory Systems
ISSN
0169-7439
Citation
Chemometrics and Intelligent Laboratory Systems vol. 193
Keywords
Fused approach
;
K-means clustering
;
Large p small n
;
Multivariate analysis
;
Seeded reduction
Publisher
Elsevier B.V.
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
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.
DOI
10.1016/j.chemolab.2019.103828
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