Multi-view Deep Subspace Clustering Networks

arXiv:1908.01978v2 Announce Type: replace-cross
Abstract: Multi-view subspace clustering aims to discover the inherent structure of data by fusing multiple views of complementary information. Most existing methods first extract multiple types of handcrafted features and then learn a joint affinity matrix for clustering. The disadvantage of this approach lies in two aspects: 1) multi-view relations are not embedded into feature learning, and 2) the end-to-end learning manner of deep learning is not suitable for multi-view clustering. Even when deep features have …

abstract arxiv clustering cs.cv cs.lg data extract features information learn lies matrix multiple networks relations type types view