research-article

Unified One-Step Multi-View Spectral Clustering

Authors:

En ZhuAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 35, Issue 6

Pages 6449 - 6460

https://doi.org/10.1109/TKDE.2022.3172687

Published: 01 June 2023 Publication History

Abstract

Multi-view spectral clustering, which exploits the complementary information among graphs of diverse views to obtain superior clustering results, has attracted intensive attention recently. However, most existing multi-view spectral clustering methods obtain the clustering partitions in a two-step scheme, i.e., spectral embedding and subsequent <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="tang-ieq1-3172687.gif"/></alternatives></inline-formula>-means. This two-step scheme inevitably seeks sub-optimal clustering results due to the information loss during the two-steps processes. Besides, existing multi-view spectral clustering methods do not jointly utilize the information of graphs and embedding matrices, which also degrades final clustering results. To solve these issues, we propose a unified one-step multi-view spectral clustering method, which integrates the spectral embedding and <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="tang-ieq2-3172687.gif"/></alternatives></inline-formula>-means into a unified framework to obtain discrete clustering labels with a one-step strategy. Under the observation that the inner product of the embedding matrix is a low-rank approximation of the graph, we combine graphs and embedding matrices of different views to obtain a unified graph. Then, we directly capture the discrete clustering indicator matrix from the unified graph. Furthermore, we design an effective optimization algorithm to solve the resultant problem. Finally, a set of experiments on various datasets are conducted to verify the effectiveness of the proposed method. The demo code of this work is publicly available at <styled-content style="color:[">r</styled-content>gb]0,0,1 <uri>https://github.com/guanyuezhen/UOMvSC</uri>.

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 35, Issue 6

June 2023

1074 pages

ISSN:1041-4347

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1041-4347 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 June 2023

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Yu XJiang YChao GChu D(2025)Deep Contrastive Multi-View Subspace Clustering With Representation and Cluster Interactive LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348416137:1(188-199)Online publication date: 1-Jan-2025
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Huang SCai HDai HFeng WLv JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Adaptive Instance-wise Multi-view ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681335(5299-5307)Online publication date: 28-Oct-2024
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Li RHu HDu LChen JJiang BZhou PCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)One-Stage Fair Multi-View Spectral ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681162(1407-1416)Online publication date: 28-Oct-2024
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