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.
Bibliographical noteFunding Information:
For Keunbaik Lee and Jae Keun Yoo (the corresponding author), this work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Education ( NRF-2019R1F1A1058553/2019R1A6A1A11051177 ) and ( NRF-2017R1A2B1004909 ), respectively. For Yuri Choi, this work was supported by the BK21 Plus Project through the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Education ( 22A20130011003 ).
© 2019 Elsevier B.V.
- Fused approach
- K-means clustering
- Large p small n
- Multivariate analysis
- Seeded reduction