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Scalable Auto-weighted Discrete Multi-view Clustering

Authors:

Liangliang Zhang,

Yuhua TangAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 3269 - 3278

https://doi.org/10.1145/3442381.3449956

Published: 03 June 2021 Publication History

Abstract

Multi-view clustering has been widely studied in machine learning, which uses complementary information to improve clustering performance. However, challenges remain when handling large-scale multi-view data due to the traditional approaches’ high time complexity. Besides, the existing approaches suffer from parameter selection. Due to the lack of labeled data, parameter selection in practical clustering applications is difficult, especially in big data. In this paper, we propose a novel approach for large-scale multi-view clustering to overcome the above challenges. Our approach focuses on learning the low-dimensional binary embedding of multi-view data, preserving the samples’ local structure during binary embedding, and optimizing the embedding and clustering in a unified framework. Furthermore, we proposed to learn the parameters using a combination of data-driven and heuristic approaches. Experiments on five large-scale multi-view datasets show that the proposed method is superior to the state-of-the-art in terms of clustering quality and running time.

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Cited By

Lao JHuang DWang CLai J(2024)Towards Scalable Multi-View Clustering via Joint Learning of Many Bipartite GraphsIEEE Transactions on Big Data10.1109/TBDATA.2023.332504510:1(77-91)Online publication date: Feb-2024
https://doi.org/10.1109/TBDATA.2023.3325045
Wang HChen RShu ZZhang YLi H(2024)Supervised adaptive similarity consistent latent representation hashingNeurocomputing10.1016/j.neucom.2023.127113570:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127113
Yun YLi JGao QYang MGao X(2023)Low-rank discrete multi-view spectral clusteringNeural Networks10.1016/j.neunet.2023.06.038166:C(137-147)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.neunet.2023.06.038
Show More Cited By

Scalable Auto-weighted Discrete Multi-view Clustering
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
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Published: 03 June 2021

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WWW '21: The Web Conference 2021

April 19 - 23, 2021

Ljubljana, Slovenia

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Cited By

Lao JHuang DWang CLai J(2024)Towards Scalable Multi-View Clustering via Joint Learning of Many Bipartite GraphsIEEE Transactions on Big Data10.1109/TBDATA.2023.332504510:1(77-91)Online publication date: Feb-2024
https://doi.org/10.1109/TBDATA.2023.3325045
Wang HChen RShu ZZhang YLi H(2024)Supervised adaptive similarity consistent latent representation hashingNeurocomputing10.1016/j.neucom.2023.127113570:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127113
Yun YLi JGao QYang MGao X(2023)Low-rank discrete multi-view spectral clusteringNeural Networks10.1016/j.neunet.2023.06.038166:C(137-147)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.neunet.2023.06.038
Ma ZWong WZhang L(2023)Binary multi-view clustering with spectral embeddingNeurocomputing10.1016/j.neucom.2023.126733557:COnline publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.126733
Huang LFan XXia TLi YDing Y(2023) SC -Net: Self-supervised learning for multi-view complementarity representation and consistency fusion network Neurocomputing10.1016/j.neucom.2023.126695556(126695)Online publication date: Nov-2023
https://doi.org/10.1016/j.neucom.2023.126695

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