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On the complexity of switching linear regression (1510.06920v2)
Published 23 Oct 2015 in stat.ML, cs.CC, and cs.LG
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.