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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
유재근scopus
Issue Date
2019
Journal Title
Chemometrics and Intelligent Laboratory Systems
ISSN
0169-7439JCR Link
Citation
Chemometrics and Intelligent Laboratory Systems vol. 193
Keywords
Fused approachK-means clusteringLarge p small nMultivariate analysisSeeded reduction
Publisher
Elsevier B.V.
Indexed
SCI; SCIE; SCOPUS 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
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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