research-article

Partial Multi-view Subspace Clustering

Authors:

Xiangyang LuoAuthors Info & Claims

MM '18: Proceedings of the 26th ACM international conference on Multimedia

Pages 1794 - 1801

https://doi.org/10.1145/3240508.3240679

Published: 15 October 2018 Publication History

Abstract

For many real-world multimedia applications, data are often described by multiple views. Therefore, multi-view learning researches are of great significance. Traditional multi-view clustering methods assume that each view has complete data. However, missing data or partial data are more common in real tasks, which results in partial multi-view learning. Therefore, we propose a novel multi-view clustering method, called Partial Multi-view Subspace Clustering (PMSC), to address the partial multi-view problem. Unlike most existing partial multi-view clustering methods that only learn a new representation of the original data, our method seeks the latent space and performs data reconstruction simultaneously to learn the subspace representation. The learned subspace representation can reveal the underlying subspace structure embedded in original data, leading to a more comprehensive data description. In addition, we enforce the subspace representation to be non-negative, yielding an intuitive weight interpretation among different data. The proposed method can be optimized by the Augmented Lagrange Multiplier (ALM) algorithm. Experiments on one synthetic dataset and four benchmark datasets validate the effectiveness of PMSC under the partial multi-view scenario.

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Cited By

Qin YZhang XYu SFeng G(2025)A survey on representation learning for multi-view dataNeural Networks10.1016/j.neunet.2024.106842181(106842)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106842
Gao JLi PLaghari ASrivastava GGadekallu TAbbas SZhang J(2024)Incomplete Multiview Clustering via Semidiscrete Optimal Transport for Multimedia Data Mining in IoTACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362554820:6(1-20)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3625548
Wang HYao MChen YXu YLiu HJia WFu XWang Y(2024)Manifold-Based Incomplete Multi-View Clustering via Bi-Consistency GuidanceIEEE Transactions on Multimedia10.1109/TMM.2024.340565026(10001-10014)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3405650
Show More Cited By

Index Terms

Partial Multi-view Subspace Clustering
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations

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Published In

cover image ACM Conferences

MM '18: Proceedings of the 26th ACM international conference on Multimedia

October 2018

2167 pages

ISBN:9781450356657

DOI:10.1145/3240508

General Chairs:
Susanne Boll
University of Oldenburg, Germany
,
Kyoung Mu Lee
Seoul National University, Korea
,
Jiebo Luo
University of Rochester, USA
,
Wenwu Zhu
Tsinghua University, China
,
Program Chairs:
Hyeran Byun
Yonsei University, Korea
,
Chang Wen Chen
State Univ. Of New York at Buffalo, USA
,
Rainer Lienhart
University of Augsburg, Germany
,
Tao Mei
JD AI, China

Copyright © 2018 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Published: 15 October 2018

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MM '18: ACM Multimedia Conference

October 22 - 26, 2018

Seoul, Republic of Korea

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MM '18 Paper Acceptance Rate 209 of 757 submissions, 28%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Qin YZhang XYu SFeng G(2025)A survey on representation learning for multi-view dataNeural Networks10.1016/j.neunet.2024.106842181(106842)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106842
Gao JLi PLaghari ASrivastava GGadekallu TAbbas SZhang J(2024)Incomplete Multiview Clustering via Semidiscrete Optimal Transport for Multimedia Data Mining in IoTACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362554820:6(1-20)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3625548
Wang HYao MChen YXu YLiu HJia WFu XWang Y(2024)Manifold-Based Incomplete Multi-View Clustering via Bi-Consistency GuidanceIEEE Transactions on Multimedia10.1109/TMM.2024.340565026(10001-10014)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3405650
Liang CWang DZhang HZhang SGuo F(2024)Robust Tensor Subspace Learning for Incomplete Multi-View ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339970736:11(6934-6948)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3399707
Zhou PDu LLiu XLing ZJi XLi XShen Y(2024)Partial Clustering EnsembleIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3321913(1-14)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3321913
Zhuge WLuo TFan RTao HHou CYi D(2024)Absent Multiview Semisupervised ClassificationIEEE Transactions on Cybernetics10.1109/TCYB.2023.324117154:3(1708-1721)Online publication date: Mar-2024
https://doi.org/10.1109/TCYB.2023.3241171
Gao JLiu MLi PLaghari AJaved AVictor NGadekallu T(2024)Deep Incomplete Multiview Clustering via Information Bottleneck for Pattern Mining of Data in Extreme-Environment IoTIEEE Internet of Things Journal10.1109/JIOT.2023.332527211:16(26700-26712)Online publication date: 15-Aug-2024
https://doi.org/10.1109/JIOT.2023.3325272
Ou SXue ZLi YLiang MCai YWu J(2024)View-Category Interactive Sharing Transformer for Incomplete Multi-View Multi-Label Learning2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02593(27457-27466)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02593
Yu YSun D(2024)Incomplete Multi-View Clustering Based on Dynamic Dimensionality Reduction Weighted Graph LearningIEEE Access10.1109/ACCESS.2024.335868112(19087-19099)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3358681
Guo XLu G(2024)Comprehensive consensus representation learning for incomplete multiview subspace clusteringInformation Sciences10.1016/j.ins.2024.120935678(120935)Online publication date: Sep-2024
https://doi.org/10.1016/j.ins.2024.120935
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