Differentiated Anchor Quantity Assisted Incomplete Multiview Clustering Without Number-Tuning

Shengju Yu, Pei Zhang, Siwei Wang, Zhibin Dong, Hengfu Yang, En Zhu, Xinwang Liu

Research output: Contribution to journalArticlepeer-review

Abstract

Incomplete multiview clustering (IMVC) generally requires the number of anchors to be the same in all views. Also, this number needs to be tuned with extra manual efforts. This not only degenerates the diversity of multiview data but also limits the model's scalability. For generating differentiated numbers of anchors without tuning, in this article we devise a novel framework named DAQINT. To be specific, the most perfect solution is to jointly find the optimal number of anchors that belongs to respective view. Regretfully, it is extremely time consuming. In view of this, we choose to first offer a set of anchor numbers for each view, and then integrate their contributions by adaptive weighting to approximate the optimal number. In particular, these offered numbers are all predefined and do not require any tuning. Through adaptively weighting them, we hold that this equivalently makes each view enjoy a different number of anchors. Accordingly, the bipartite graphs generated on all views are with diverse scales. Besides exploring multiview features more deeply, they also balance the importance between views. Then, to fuse these multiscale bipartite graphs, we design a combination strategy that owns linear computation and storage overheads. Afterward, to solve the resulting optimization problem, we also carefully develop a three-step iterative algorithm with linear complexities and demonstrated convergence. Experiments on the multiple public datasets validate the superiority of DAQINT against several advanced IMVC methods, such as on Mfeat, DAQINT surpasses the competitors like MKC, EEIMVC, FLSD, DSIMVC, IMVC-CBG, and DCP by 36.65%, 6.33%, 48.53%, 22.46%, 15.06%, and 32.04%, respectively, in ACC.

Original languageEnglish
Pages (from-to)7024-7037
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume54
Issue number11
DOIs
StatePublished - 2024
Externally publishedYes

Funding

Manuscript received 12 April 2024; revised 19 July 2024; accepted 5 August 2024. Date of publication 22 August 2024; date of current version 30 October 2024. This work was supported in part by the National Key Research and Development Program of China under Project 2022ZD0209103, and in part by the National Natural Science Foundation of China under Project 62325604 and Project 62276271. This article was recommended by Associate Editor P. Shi. (Corresponding authors: Siwei Wang; En Zhu; Xinwang Liu.) Shengju Yu, Pei Zhang, Zhibin Dong, En Zhu, and Xinwang Liu are with the School of Computer, National University of Defense Technology, Changsha 410073, China (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]).

FundersFunder number
National Key Research and Development Program of China2022ZD0209103
National Key Research and Development Program of China
National Natural Science Foundation of China62325604, 62276271
National Natural Science Foundation of China

    Keywords

    • Bipartite graph learning
    • incomplete multiview clustering (IMVC)
    • large-scale clustering
    • multiview clustering

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