On the complexity of switching linear regression
Machine Learning
2016-07-05 v2 Computational Complexity
Machine Learning
Abstract
This technical note extends recent results on the computational complexity of globally minimizing the error of piecewise-affine models to the related problem of minimizing the error of switching linear regression models. In particular, we show that, on the one hand the problem is NP-hard, but on the other hand, it admits a polynomial-time algorithm with respect to the number of data points for any fixed data dimension and number of modes.
Cite
@article{arxiv.1510.06920,
title = {On the complexity of switching linear regression},
author = {Fabien Lauer},
journal= {arXiv preprint arXiv:1510.06920},
year = {2016}
}
Comments
Automatica, Elsevier, 2016