research-article

Scalable Incomplete Multi-View Clustering with Structure Alignment

Authors:

En ZhuAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 3031 - 3040

https://doi.org/10.1145/3581783.3611981

Published: 27 October 2023 Publication History

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. Our code is publicly available at https://github.com/wy1019/SIMVC-SA.

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Cited By

Wan XXiao BLiu XLiu JLiang WZhu E(2024)Fast Continual Multi-View Clustering With Incomplete ViewsIEEE Transactions on Image Processing10.1109/TIP.2024.338897433(2995-3008)Online publication date: 2024
https://doi.org/10.1109/TIP.2024.3388974
Liu SLiang KDong ZWang SYang XZhou SZhu ELiu X(2024)Learn from View Correlation: An Anchor Enhancement Strategy for Multi-View Clustering2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02471(26151-26161)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02471

Index Terms

Scalable Incomplete Multi-View Clustering with Structure Alignment
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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the National Key R\&D Program of China
the National Natural Science Foundation of China

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MM '23

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

Wan XXiao BLiu XLiu JLiang WZhu E(2024)Fast Continual Multi-View Clustering With Incomplete ViewsIEEE Transactions on Image Processing10.1109/TIP.2024.338897433(2995-3008)Online publication date: 2024
https://doi.org/10.1109/TIP.2024.3388974
Liu SLiang KDong ZWang SYang XZhou SZhu ELiu X(2024)Learn from View Correlation: An Anchor Enhancement Strategy for Multi-View Clustering2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02471(26151-26161)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02471

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