Multiple Partitions Alignment via Spectral Rotation

Shudong Huang (Sichuan University)*; Ivor Tsang (University of Technology Sydney); Zenglin Xu (Harbin Institute of Technology); Jiancheng Lv (Sichuan University)


Multi-view spectral clustering has drawn much attention due to the effectiveness of exploiting the similarity relationships among data points. These methods typically reveal the intrinsic structure using a predefined graph for each view. The predefined graphs are fused to a consensus one, on which the final clustering results are obtained. However, such common strategies may lead to information loss because of the inconsistency or noise among multiple views. In this paper, we propose to merge multi-view information in partition level instead of the raw feature space where the data points lie. The partition of each view is treated as a perturbation of the consensus clustering, and the multiple partitions are integrated by estimating a distinct rotation for each partition. The proposed model is formulated as a joint learning framework, i.e., with the input data matrix, our model directly outputs the final discrete clustering result. Hence it is an end-to-end single-stage learning model. An iterative updating algorithm is proposed to solve the learning problem, in which the involved variables can be optimized in a mutual reinforcement manner. Experimental results on real-world data sets illustrate the effectiveness of our model.