Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/6540

Improved independent component regression modeling

Authors Zhao, C.
Gao, F.
Liu, T.
Wang, F.
Issue Date 2009
Source Proceedings of the IEEE Conference on Decision and Control , 2009, p. 1507-1512
Summary The conventional independent component regression (ICR), as an exclusive two-step implementation algorithm, has the risk similar to principal component regression (PCR). That is, the extracted independent components (ICs) are not guaranteed to be informative with respect to quality prediction and interpretation. Moreover, it inherits some inconveniences of conventional ICA. In this paper, first, the drawbacks of original ICR are analyzed. Then a modified ICR (M-ICR) modeling algorithm is developed. To enhance the causal relationship between the extracted ICs and quality variables, a dual-objective optimization solution is constructed in the first-step feature extraction modeling. It simultaneously considers two-fold statistical requirements, the independence and quality-correlation. Moreover, their different roles in calibration modeling can be quantitatively evaluated by flexibly adjusting the sub-optimization objective weights. The practicability and performance of M-ICR are illustrated and discussed in simulation experiment. ©2009 IEEE.
Subjects
ISSN 0191-2216
Rights © 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Language English
Format Conference paper
Access View full-text via DOI
View full-text via Scopus
Find@HKUST
Files in this item:
File Description Size Format
ImprovedIndepen.pdf 548123 B Adobe PDF