Fine-grained Graph Learning for Multi-view Subspace Clustering. (arXiv:2201.04604v1 [cs.LG])

Multi-view subspace clustering has conventionally focused on integrating
heterogeneous feature descriptions to capture higher-dimensional information.
One popular strategy is to generate a common subspace from different views and
then apply graph-based approaches to deal with clustering. However, the
performance of these methods is still subject to two limitations, namely the
multiple views fusion pattern and the connection between the fusion process and
clustering tasks. To address these problems, we propose a novel multi-view
subspace clustering framework via fine-grained graph learning, …

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