A New Regularization Classification Method Based on Extreme Learning Machine in Network Data

摘要

In this paper, we present a new regularization classification method based on extreme learning machine for within network node classification problem. In particular, we define a new objective function, which contains loss function, smoothness function, and intra-class regularization and inter-class regularization terms. In realization, we extend extreme learning machine to semi-supervised learning problem and deduce a new hidden layer weight, and then use the advanced extreme learning machine to optimize the objective function. Experiment results shows that our method can obtain higher accuracies than other methods when more than 30% nodes are labeled in the network.