Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
393279 | Information Sciences | 2015 | 18 Pages |
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 linear model in each partition. The proposed algorithm is similar in spirit to k-means clustering algorithm. We show that our algorithm can also be viewed as a special case of an EM algorithm for maximum likelihood estimation under a reasonable probability model. We empirically demonstrate the effectiveness of our approach by comparing its performance with that of the state of art algorithms on various datasets.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Naresh Manwani, P.S. Sastry,