  Multi-view subspace clustering (MSC) is a popular unsupervised method by
integrating heterogeneous information to reveal the intrinsic clustering
structure hidden across views. Usually, MSC methods use graphs (or affinity
matrices) fusion to learn a common structure, and further apply graph-based
approaches to clustering. Despite progress, most of the methods do not
establish the connection between graph learning and clustering. Meanwhile,
conventional graph fusion strategies assign coarse-grained weights to combine
multi-graph, ignoring the importance of local structure. In this paper, we
propose a fine-grained graph learning framework for multi-view subspace
clustering (FGL-MSC) to address these issues. To utilize the multi-view
information sufficiently, we design a specific graph learning method by
introducing graph regularization and a local structure fusion pattern. The main
challenge is how to optimize the fine-grained fusion weights while generating
the learned graph that fits the clustering task, thus making the clustering
representation meaningful and competitive. Accordingly, an iterative algorithm
is proposed to solve the above joint optimization problem, which obtains the
learned graph, the clustering representation, and the fusion weights
simultaneously. Extensive experiments on eight real-world datasets show that
the proposed framework has comparable performance to the state-of-the-art
methods. The source code of the proposed method is available at
https://github.com/siriuslay/FGL-MSC.
