Bipartite graph based spectral rotation with fuzzy anchors

Abstract

Spectral clustering usually obtains impressive performance owing to the usage of the manifold structure of data. However, it is infeasible to widely apply conventional spectral clustering methods due to the high computational complexity. To address this challenging problem, a series of extensions like anchor points based models have been developed. Nevertheless, the existing approaches separate anchor points selection and the construction of the bipartite graph, which hinders the performance of the anchor-based methods. In this paper, by revealing the connection between the existing anchor-based spectral clustering and fuzzy k-means, the performance of related methods is found to be limited by the early termination. Accordingly, we proposed a complete learning approach to generate high-quality bipartite graphs. Furthermore, the produced bipartite graph can be directly utilized to perform spectral clustering, and the improved spectral rotation is employed to further boost the performance. Eventually, extensive experiments illustrate that the proposed method obtains good performance especially when the anchor points are limited.