Piecewise nonlinear regression via decision adaptive trees

Date
2014-09
Editor(s)
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
European Signal Processing Conference
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
1188 - 1192
Language
English
Journal Title
Journal ISSN
Volume Title
Series
Abstract

We investigate the problem of adaptive nonlinear regression and introduce tree based piecewise linear regression algorithms that are highly efficient and provide significantly improved performance with guaranteed upper bounds in an individual sequence manner. We partition the regressor space using hyperplanes in a nested structure according to the notion of a tree. In this manner, we introduce an adaptive nonlinear regression algorithm that not only adapts the regressor of each partition but also learns the complete tree structure with a computational complexity only polynomial in the number of nodes of the tree. Our algorithm is constructed to directly minimize the final regression error without introducing any ad-hoc parameters. Moreover, our method can be readily incorporated with any tree construction method as demonstrated in the paper. © 2014 EURASIP.

Course
Other identifiers
Book Title
Citation
Published Version (Please cite this version)