LAUSR

Abstract Clustering generic data, i.e., data not specific to a particular field, is a challenging problem due to their diverse complex structures in the original feature space. Traditional approaches address this problem by complementing clustering with feature learning methods, which either capture the intrinsic structure of the data or represent the data such that clusters are better revealed. In this paper, we propose an approach referred to as Extreme Learning Machine for Joint Embedding and Clustering (ELM-JEC), which incorporates desirable properties of both types of feature learning methods at the same time, specifically by (1) preserving the manifold structure of the data in the original space; (2) maximizing the class separability of the data in the embedded space. Since either type of method has improved clustering performance in some cases, our motivation is to integrate the two desirable properties to further improve the accuracy and robustness of clustering. Additional notable features of ELM-JEC are that it provides nonlinear feature mappings and achieves feature learning and clustering in the same formulation. The proposed approach can be implemented using alternating optimization, and its clustering performance compares favorably with several state-of-the-art methods on the real-world benchmark datasets.