Self-Supervised Deep Multi-View Subspace Clustering

As a new occurring unsupervised method, multi-view clustering offers a good way to investigate the hidden structure from multi-view data and attracts extensive attention in the community of machine learning and data mining. One popular approach is to identify a common latent subspace for capturing the multi-view information. However, these methods are still limited due to the unsupervised learning process and suffer from considerable noisy information from different views. To address this issue, we present a novel multi-view subspace clustering method, named self-supervised deep multi-view subspace clustering (S2DMVSC). It seamlessly integrates spectral clustering and affinity learning into a deep learning framework. S2DMVSC has two main merits. One is that the clustering results can be sufficiently exploited to supervise the latent representation learning for each view (via a classification loss) and the common latent subspace learning (via a spectral clustering loss) for multiple views. The other is that the affinity matrix among data objects is automatically computed according to the high-level and cluster-driven representation. Experiments on two scenarios, including original features and multiple hand-crafted features, demonstrate the superiority of the proposed approach over the state-of-the-art baselines.