Scalable Incomplete Multi-View Clustering with Structure Alignment
Yi Wen Siwei Wang Ke Liang Weixuan Liang Xinhang Wan Xinwang Liu Suyuan Liu Jiyuan Liu En Zhu
The success of existing multi-view clustering (MVC) relies on the assumption
that all views are complete. However, samples are usually partially available
due to data corruption or sensor malfunction, which raises the research of
incomplete multi-view clustering (IMVC). Although several anchor-based IMVC
methods have been proposed to process the large-scale incomplete data, they
still suffer from the following drawbacks: i) Most existing approaches neglect
the inter-view discrepancy and enforce cross-view representation to be
consistent, which would corrupt the representation capability of the model; ii)
Due to the samples disparity between different views, the learned anchor might
be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete
data (AUP-ID). Such the AUP-ID would cause inaccurate graph fusion and degrades
clustering performance. To tackle these issues, we propose a novel incomplete
anchor graph learning framework termed Scalable Incomplete Multi-View
Clustering with Structure Alignment (SIMVC-SA). Specially, we construct the
view-specific anchor graph to capture the complementary information from
different views. In order to solve the AUP-ID, we propose a novel structure
alignment module to refine the cross-view anchor correspondence. Meanwhile, the
anchor graph construction and alignment are jointly optimized in our unified
framework to enhance clustering quality. Through anchor graph construction
instead of full graphs, the time and space complexity of the proposed SIMVC-SA
is proven to be linearly correlated with the number of samples. Extensive
experiments on seven incomplete benchmark datasets demonstrate the
effectiveness and efficiency of our proposed method. Our code is publicly
available at https://github.com/wy1019/SIMVC-SA.