  This paper is concerned with the sparsification of the input-hidden weights
of ELM (Extreme Learning Machine). For ordinary feedforward neural networks,
the sparsification is usually done by introducing certain regularization
technique into the learning process of the network. But this strategy can not
be applied for ELM, since the input-hidden weights of ELM are supposed to be
randomly chosen rather than to be learned. To this end, we propose a modified
ELM, called ELM-LC (ELM with local connections), which is designed for the
sparsification of the input-hidden weights as follows: The hidden nodes and the
input nodes are divided respectively into several corresponding groups, and an
input node group is fully connected with its corresponding hidden node group,
but is not connected with any other hidden node group. As in the usual ELM, the
hidden-input weights are randomly given, and the hidden-output weights are
obtained through a least square learning. In the numerical simulations on some
benchmark problems, the new ELM-CL behaves better than the traditional ELM.
