research-article

Incomplete Multi-View Clustering with Regularized Hierarchical Graph

Authors:

Pengyang ZhaoAuthors Info & Claims

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

Pages 3060 - 3068

https://doi.org/10.1145/3581783.3612241

Published: 27 October 2023 Publication History

Abstract

In this article, we propose a novel and effective incomplete multi-view clustering (IMVC) framework, referred to as incomplete multi-view clustering with regularized hierarchical graph (IMVC_RHG). Different from the existing graph learning-based IMVC methods, IMVC_RHG introduces a novel heterogeneous-graph learning and embedding strategy, which adopts the high-order structures between four tuples for each view, rather than a simple paired-sample intrinsic structure. Besides this, with the aid of the learned heterogeneous graphs, a between-view preserving strategy is designed to recover the incomplete graph for each view. Finally, a consensus representation for each sample is gained with a co-regularization term for final clustering. As a result of integrating these three learning strategies, IMVC_RHG can be flexibly applied to different types of IMVC tasks. Comparing with the other state-of-the-art methods, the proposed IMVC_RHG can achieve the best performances on real-world incomplete multi-view databases.

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This article proposes an incomplete multi-view clustering (IMVC) framework, referred to as incomplete multi-view clustering with regularized hierarchical graph (IMVC_RHG). It can precisely construct the complete graphs, preserve the global structures for all views, and flexibly handle all types of incomplete multi-view data.

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Cited By

Che HPu XOuyang DLi BCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Enhanced Tensorial Self-representation Subspace Learning for Incomplete Multi-view ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681573(719-728)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681573
Bai RHuang RChen YQin YXu YZheng Q(2024)A hierarchical consensus learning model for deep multi-view document clusteringInformation Fusion10.1016/j.inffus.2024.102507111:COnline publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102507

Index Terms

Incomplete Multi-View Clustering with Regularized Hierarchical Graph
1. 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
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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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Science and Technology Development Fund of Macau
University of Macau
National Natural Science Foundation of China
CAAI-Huawei MindSpore Open Fund

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MM '23

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SIGMM

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

October 29 - November 3, 2023

Ottawa ON, Canada

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Cited By

Che HPu XOuyang DLi BCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Enhanced Tensorial Self-representation Subspace Learning for Incomplete Multi-view ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681573(719-728)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681573
Bai RHuang RChen YQin YXu YZheng Q(2024)A hierarchical consensus learning model for deep multi-view document clusteringInformation Fusion10.1016/j.inffus.2024.102507111:COnline publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102507

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