Extreme learning machine based mutual information estimation with application to time-series change-points detection

In this paper, we propose an efficient parameter tuning-free squared-loss mutual information (SMI) estimator in a form of a radial basis function (RBF) network. The input layer of the proposed network propagates a sample pair of two random variables to the hidden layer. The propagated samples are then transformed by a set of Gaussian RBF kernels with randomly determined kernel centers and widths similar to that in an extreme learning machine. The output layer adopts a linear weighting scheme which can be analytically estimated. Our empirical results show that the proposed estimator outperforms the competing state-of-the-art SMI estimators in terms of computational efficiency while showing the comparable estimation accuracy performance. Moreover, the proposed model achieves promising results in an application study of time-series change-points detection and driving stress.