K-Plane Regression
Abstract
In this paper, we present a novel algorithm for piecewise linear regression which can learn continuous as well as discontinuous piecewise linear functions. The main idea is to repeatedly partition the data and learn a liner model in in each partition. While a simple algorithm incorporating this idea does not work well, an interesting modification results in a good algorithm. The proposed algorithm is similar in spirit to -means clustering algorithm. We show that our algorithm can also be viewed as an EM algorithm for maximum likelihood estimation of parameters under a reasonable probability model. We empirically demonstrate the effectiveness of our approach by comparing its performance with the state of art regression learning algorithms on some real world datasets.
Cite
@article{arxiv.1211.1513,
title = {K-Plane Regression},
author = {Naresh Manwani and P. S. Sastry},
journal= {arXiv preprint arXiv:1211.1513},
year = {2014}
}