research-article

Partial Tubal Nuclear Norm Regularized Multi-view Learning

Authors:

Yicong ZhouAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 1341 - 1349

https://doi.org/10.1145/3474085.3475699

Published: 17 October 2021 Publication History

Abstract

Multi-view clustering and multi-view dimension reduction explore ubiquitous and complementary information between multiple features to enhance the clustering, recognition performance. However, multi-view clustering and multi-view dimension reduction are treated independently, ignoring the underlying correlations between them. In addition, previous methods mainly focus on using the tensor nuclear norm for low-rank representation to explore the high correlation of multi-view features, which often causes the estimation bias of the tensor rank. To overcome these limitations, we propose the partial tubal nuclear norm regularized multi-view learning (PTN2ML) method, in which the partial tubal nuclear norm as a non-convex surrogate of the tensor tubal multi-rank, only minimizes the partial sum of the smaller tubal singular values to preserve the low-rank property of the self-representation tensor. PTN2ML pursues the latent representation from the projection space rather than from the input space to reveal the structural consensus and suppress the disturbance of noisy data. The proposed method can be efficiently optimized by the alternating direction method of multipliers. Extensive experiments, including multi-view clustering and multi-view dimension reduction substantiate the superiority of the proposed methods beyond state-of-the-arts.

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Cited By

Chen YZhao YWang SChen JZhang Z(2024)Partial Tubal Nuclear Norm-Regularized Multiview Subspace LearningIEEE Transactions on Cybernetics10.1109/TCYB.2023.326317554:6(3777-3790)Online publication date: Jun-2024
https://doi.org/10.1109/TCYB.2023.3263175
Zhang CXue HNie KWu XLou ZYang SZhou QHu S(2024)Nice to meet images with Big Clusters and Features: A cluster-weighted multi-modal co-clustering methodInformation Processing & Management10.1016/j.ipm.2024.10373561:5(103735)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103735
Chen ZFu LXiao SWang SPlant CGuo W(2023)Multi-View Graph Convolutional Networks with Differentiable Node SelectionACM Transactions on Knowledge Discovery from Data10.1145/360895418:1(1-21)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3608954
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Index Terms

Partial Tubal Nuclear Norm Regularized Multi-view Learning
1. Computing methodologies

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cover image ACM Conferences

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

Copyright © 2021 ACM.

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Published: 17 October 2021

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the Shenzhen College Stability Support Plan
the Guangdong Basic and Applied Basic Research Foundation
University of Macau
the Shenzhen Key Technical Project

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MM '21

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MM '21: ACM Multimedia Conference

October 20 - 24, 2021

Virtual Event, China

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

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MM '24

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The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

Melbourne , VIC , Australia

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Cited By

Chen YZhao YWang SChen JZhang Z(2024)Partial Tubal Nuclear Norm-Regularized Multiview Subspace LearningIEEE Transactions on Cybernetics10.1109/TCYB.2023.326317554:6(3777-3790)Online publication date: Jun-2024
https://doi.org/10.1109/TCYB.2023.3263175
Zhang CXue HNie KWu XLou ZYang SZhou QHu S(2024)Nice to meet images with Big Clusters and Features: A cluster-weighted multi-modal co-clustering methodInformation Processing & Management10.1016/j.ipm.2024.10373561:5(103735)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103735
Chen ZFu LXiao SWang SPlant CGuo W(2023)Multi-View Graph Convolutional Networks with Differentiable Node SelectionACM Transactions on Knowledge Discovery from Data10.1145/360895418:1(1-21)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3608954
Wang SChen YLin ZCen YCao Q(2023)Robustness Meets Low-Rankness: Unified Entropy and Tensor Learning for Multi-View Subspace ClusteringIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.326680133:11(6302-6316)Online publication date: 13-Apr-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3266801
Zhang XZhang YLiu ZWang H(2023)Three-dimensional seismic data reconstruction via partial sum of tensor nuclear norm minimizationJournal of Geophysics and Engineering10.1093/jge/gxad01220:2(376-386)Online publication date: 1-Mar-2023
https://doi.org/10.1093/jge/gxad012

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