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On the complexity of switching linear regression

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.

preprint2016arXivOpen access
Fabien Lauer
Machine LearningComputational Complexity
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Fabien Lauer

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