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In addition to various spatial fusion-based methods, an affinity fusion-based network is also proposed in which the self-expressive layer corresponding to different modalities is enforced to be the same.
Ranked #1 on Image Clustering on Extended Yale-B
Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral clustering algorithm to handle multi-view data.
A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years.
Multi-view clustering is an important and fundamental problem.
To this end, this paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which boosts the clustering performance by exploring the consensus information of multi-view data.
However, different views often have distinct incompleteness, i. e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views).
Furthermore, underlying graph information of multi-view data is always ignored in most existing multi-view subspace clustering methods.