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arXiv · 1211.1513

K-Plane Regression

Naresh Manwani·P. S. Sastry
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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 $k$-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.

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Naresh Manwani, P. S. Sastry. 2013-03-27. K-Plane Regression. https://doi.org/10.1016/j.ins.2014.08.058

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