Exsavi: Excavating both sample-wise and view-wise relationships to boost multi-view subspace clustering

摘要

Multi-view clustering aims to partition objects based on multiple views through unsupervised learning. To exploit cross-view information, recent advances have shifted the focus from matrix-based to tensorbased subspace learning. Though, both approaches leverage the subspace representation of the data, further higher-order statistics extraction is often overlooked. To tackle the issue, in this paper, a novel multiview clustering method is proposed to extract both intrinsic sample-wise and view-wise statistics from multi-view data. The multi-view data is represented in tensor form and mapped into a latent tensor subspace to exploit its sample-wise relationship. Through using multi-dimensional sparse coding in its similarity tensor, the view-wise relationship is exploited and evacuated. The two relationships are jointly learned and a latent clustering structure is essentially captured in a data-driven way. We conducted extensive experiments on face, object, digital ima ge, and text datasets and compared with ten stateof-the-art methods. The experimental results demonstrated that the proposed method, named as Exsavi, outperform the baselines in terms of various evaluation metrics. (c) 2020 Published by Elsevier B.V.