Scalable Incomplete Multi-View Clustering with Structure Alignment

An official source code for paper Scalable Incomplete Multi-View Clustering with Structure Alignment (SIMVC-SA), accepted by ACM MM 2023. Any communications or issues are welcomed. Please contact [email protected].

Abstract

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.

Main function

• run.m

Datasets

• dataset/BDGP_fea_Per0.1.mat

• dataset/BDGP_fea_Per0.2.mat

  . . .

• dataset/BDGP_fea_Per0.9.mat

Citations

If you find this repository helpful, please cite our paper:

@inproceedings{wen2023scalable,
  title={Scalable Incomplete Multi-View Clustering with Structure Alignment},
  author={Wen, Yi and Wang, Siwei and Liang, Ke and Liang, Weixuan and Wan, Xinhang and Liu, Xinwang and Liu, Suyuan and Liu, Jiyuan and Zhu, En},
  booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
  pages={3031--3040},
  year={2023}
}