Unsupervised Feature Selection based Extreme Learning Machine for Clustering

Abstract For data with various complicated distribution in the original feature space, it is difficult to find the clusters of the data. Extreme learning machine (ELM) is famous for its universal approximation capability and the hidden space created by random nonlinear feature mapping. Existing ELM based clustering methods address this by constructing an embedding space, in which the cluster are easily revealed. A commonality of them is the final results have to be subsequently derived by k-means clustering. In this paper, we propose an unsupervised feature selection based extreme learning machine (UFSELM) for clustering, which integrates ELM with L2,1 norm regularization to remove the worthless hidden neurons and clusters the data directly without building an embedding. Specifically, the proposed method conducts feature selection by minimizing the L2,1 norm of output weights, and the clustering results is computed by eigendecomposition. By solving the formulated optimization problem in an iterative fashion, we improved the accuracy of clustering. We conducted experiments on several public datasets to demonstrate the effectiveness of the proposed method and further analyzed the properties of the proposed method.