research-article

Multiview Clustering via Adaptively Weighted Procrustes

Authors:

Xuelong LiAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2022 - 2030

https://doi.org/10.1145/3219819.3220049

Published: 19 July 2018 Publication History

Abstract

In this paper, we make a multiview extension of the spectral rotation technique raised in single view spectral clustering research. Since spectral rotation is closely related to the Procrustes Analysis for points matching, we point out that classical Procrustes Average approach can be used for multiview clustering. Besides, we show that direct applying Procrustes Average (PA) in multiview tasks may not be optimal theoretically and empirically, since it does not take the clustering capacity differences of different views into consideration. Other than that, we propose an Adaptively Weighted Procrustes (AWP) approach to overcome the aforementioned deficiency. Our new AWP weights views with their clustering capacities and forms a weighted Procrustes Average problem accordingly. The optimization algorithm to solve the new model is computational complexity analyzed and convergence guaranteed. Experiments on five real-world datasets demonstrate the effectiveness and efficiency of the new models.

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References

[1]

Arthur Asuncion and David Newman . 2007. UCI machine learning repository. (2007).

[2]

Stephen Boyd and Lieven Vandenberghe . 2004. Convex optimization. Cambridge university press.

Digital Library

[3]

Xiao Cai, Feiping Nie, Heng Huang, and Farhad Kamangar . 2011. Heterogeneous image feature integration via multi-modal spectral clustering Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 1977--1984.

Digital Library

[4]

Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng . 2009. NUS-WIDE: a real-world web image database from National University of Singapore Proceedings of the ACM international conference on image and video retrieval. ACM, 48.

Digital Library

[5]

Li Fei-Fei, Rob Fergus, and Pietro Perona . 2007. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer vision and Image understanding Vol. 106, 1 (2007), 59--70.

Digital Library

[6]

Jerome Friedman, Trevor Hastie, and Robert Tibshirani . 2001. The elements of statistical learning. Vol. Vol. 1. Springer series in statistics New York.

[7]

Gene H Golub and Charles F Van Loan . 2012. Matrix computations. Vol. Vol. 3. JHU Press.

[8]

Jin Huang, Feiping Nie, and Heng Huang . 2013. Spectral Rotation versus K-Means in Spectral Clustering. AAAI.

Digital Library

[9]

Abhishek Kumar, Piyush Rai, and Hal Daume . 2011. Co-regularized multi-view spectral clustering. In Advances in neural information processing systems. 1413--1421.

Digital Library

[10]

Andrew Y Ng, Michael I Jordan, and Yair Weiss . 2002. On spectral clustering: Analysis and an algorithm. In Advances in neural information processing systems. 849--856.

Digital Library

[11]

Feiping Nie, Guohao Cai, and Xuelong Li . 2017 a. Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours. In AAAI. 2408--2414.

[12]

Feiping Nie, Jing Li, and Xuelong Li . 2016 a. Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification. IJCAI. 1881--1887.

Digital Library

[13]

Feiping Nie, Jing Li, and Xuelong Li . 2017 b. Self-weighted multiview clustering with multiple graphs Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. 2564--2570.

Digital Library

[14]

Feiping Nie, Xiaoqian Wang, Michael I Jordan, and Heng Huang . 2016 b. The Constrained Laplacian Rank Algorithm for Graph-Based Clustering Thirtieth AAAI Conference on Artificial Intelligence. Citeseer.

Digital Library

[15]

Ferdinando S Samaria and Andy C Harter . 1994. Parameterisation of a stochastic model for human face identification Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on. IEEE, 138--142.

[16]

Peter H. Schonemann . 1966. A generalized solution of the orthogonal procrustes problem. Psychometrika Vol. 31, 1 (1966), 1--10.

[17]

GW Stewart and Ji-Guang Sun . 1990. Matrix Perturbation Theory (Computer Science and Scientific Computing). (1990).

[18]

Ulrike Von Luxburg . 2007. A tutorial on spectral clustering. Statistics and computing Vol. 17, 4 (2007), 395--416.

Digital Library

[19]

John Winn and Nebojsa Jojic . 2005. Locus: Learning object classes with unsupervised segmentation Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, Vol. Vol. 1. IEEE, 756--763.

Digital Library

[20]

Rongkai Xia, Yan Pan, Lei Du, and Jian Yin . 2014. Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition. AAAI. 2149--2155.

Digital Library

[21]

Chang Xu, Dacheng Tao, and Chao Xu . 2013. A survey on multi-view learning. arXiv preprint arXiv:1304.5634 (2013).

[22]

Xiaojin Zhu, Zoubin Ghahramani, John Lafferty, et almbox. . 2003. Semi-supervised learning using gaussian fields and harmonic functions ICML, Vol. Vol. 3. 912--919.

Digital Library

Cited By

Ma LLi HZang WLiu XSun M(2025)Tensor-Based Uniform and Discrete Multi-View Projection ClusteringElectronics10.3390/electronics1404081714:4(817)Online publication date: 19-Feb-2025
https://doi.org/10.3390/electronics14040817
Tang CWang MSun K(2025)One-Step Multiview Clustering via Adaptive Graph Learning and Spectral RotationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.338122336:3(5442-5453)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3381223
Nie FLiu HWang RLi X(2025) Parameter-Free Multiview K -Means Clustering With Coordinate Descent Method IEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.337353236:3(4879-4892)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3373532
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Index Terms

Multiview Clustering via Adaptively Weighted Procrustes
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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cover image ACM Other conferences

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

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Published: 19 July 2018

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National Natural Science Foundation of China

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KDD '18

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Ma LLi HZang WLiu XSun M(2025)Tensor-Based Uniform and Discrete Multi-View Projection ClusteringElectronics10.3390/electronics1404081714:4(817)Online publication date: 19-Feb-2025
https://doi.org/10.3390/electronics14040817
Tang CWang MSun K(2025)One-Step Multiview Clustering via Adaptive Graph Learning and Spectral RotationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.338122336:3(5442-5453)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3381223
Nie FLiu HWang RLi X(2025) Parameter-Free Multiview K -Means Clustering With Coordinate Descent Method IEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.337353236:3(4879-4892)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3373532
Yang BZhang XWu JNie FLin ZWang FChen B(2025)Fast Multiview Anchor-Graph ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.335969036:3(4947-4958)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3359690
Sun WJiang ZChen YLi JZhou CHan N(2025)Deep Similarity Graph Fusion for Multiview ClusteringIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.347918812:1(435-446)Online publication date: Feb-2025
https://doi.org/10.1109/TCSS.2024.3479188
Kong DZhou SJin SYe FZhang X(2025)One-step multi-view spectral clustering based on multi-feature similarity fusionSignal Processing10.1016/j.sigpro.2024.109729227(109729)Online publication date: Feb-2025
https://doi.org/10.1016/j.sigpro.2024.109729
Zhou KZhu YGuo MJiang M(2025) MvWECM: Multi-view Weighted Evidential -Means clustering Pattern Recognition10.1016/j.patcog.2024.111108159(111108)Online publication date: Mar-2025
https://doi.org/10.1016/j.patcog.2024.111108
Li XZhang X(2025)Multi-view clustering integrating anchor attribute and structural informationNeurocomputing10.1016/j.neucom.2025.129689630(129689)Online publication date: May-2025
https://doi.org/10.1016/j.neucom.2025.129689
Chen HZhang RDuan YWang RNie F(2025)Fast multi-view clustering via anchor label transmit with tensor structure constraintExpert Systems with Applications10.1016/j.eswa.2025.126878274(126878)Online publication date: May-2025
https://doi.org/10.1016/j.eswa.2025.126878
Qing H(2025)Community detection by spectral methods in multi-layer networksApplied Soft Computing10.1016/j.asoc.2025.112769171(112769)Online publication date: Mar-2025
https://doi.org/10.1016/j.asoc.2025.112769
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