Real-time sufficient dimension reduction through principal least squares support vector machines

PATTERN RECOGNITION

卷:112

Article

Artemiou, Andreas¹  Dong, Yuexiao²  Shin, Seung Jun³ 

[1] Cardiff Univ, Sch Math, Stat, Cardiff, S Glam, Wales

[2] Temple Univ, Dept Stat Sci, Philadelphia, PA 19122 USA

[3] Korea Univ, Dept Stat, Seoul, South Korea

关键词: Central subspace;    Ladle estimator;    Online sliced inverse regression;    Principal support vector machines;    Streamed data;   

DOI  :  10.1016/j.patcog.2020.107768

来源: Elsevier

【 摘 要 】

We propose a real-time approach for sufficient dimension reduction. Compared with popular sufficient dimension reduction methods including sliced inverse regression and principal support vector machines, the proposed principal least squares support vector machines approach enjoys better estimation of the central subspace. Furthermore, this new proposal can be used in the presence of streamed data for quick real-time updates. It is demonstrated through simulations and real data applications that our proposal performs better and faster than existing algorithms in the literature. (c) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

Free