Multi-View Clustering by Inter-cluster Connectivity Guided Reward

Hao Dai, Yang Liu, Peng Su, Hecheng Cai, Shudong Huang, Jiancheng Lv
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:9846-9855, 2024.

Abstract

Multi-view clustering has been widely explored for its effectiveness in harmonizing heterogeneity along with consistency in different views of data. Despite the significant progress made by recent works, the performance of most existing methods is heavily reliant on strong priori information regarding the true cluster number $\textit{K}$, which is rarely feasible in real-world scenarios. In this paper, we propose a novel graph-based multi-view clustering algorithm to infer unknown $\textit{K}$ through a graph consistency reward mechanism. To be specific, we evaluate the cluster indicator matrix during each iteration with respect to diverse $\textit{K}$. We formulate the inference process of unknown $\textit{K}$ as a parsimonious reinforcement learning paradigm, where the reward is measured by inter-cluster connectivity. As a result, our approach is capable of independently producing the final clustering result, free from the input of a predefined cluster number. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed approach in comparison to existing state-of-the-art methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-dai24b, title = {Multi-View Clustering by Inter-cluster Connectivity Guided Reward}, author = {Dai, Hao and Liu, Yang and Su, Peng and Cai, Hecheng and Huang, Shudong and Lv, Jiancheng}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {9846--9855}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/dai24b/dai24b.pdf}, url = {https://proceedings.mlr.press/v235/dai24b.html}, abstract = {Multi-view clustering has been widely explored for its effectiveness in harmonizing heterogeneity along with consistency in different views of data. Despite the significant progress made by recent works, the performance of most existing methods is heavily reliant on strong priori information regarding the true cluster number $\textit{K}$, which is rarely feasible in real-world scenarios. In this paper, we propose a novel graph-based multi-view clustering algorithm to infer unknown $\textit{K}$ through a graph consistency reward mechanism. To be specific, we evaluate the cluster indicator matrix during each iteration with respect to diverse $\textit{K}$. We formulate the inference process of unknown $\textit{K}$ as a parsimonious reinforcement learning paradigm, where the reward is measured by inter-cluster connectivity. As a result, our approach is capable of independently producing the final clustering result, free from the input of a predefined cluster number. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed approach in comparison to existing state-of-the-art methods.} }
Endnote
%0 Conference Paper %T Multi-View Clustering by Inter-cluster Connectivity Guided Reward %A Hao Dai %A Yang Liu %A Peng Su %A Hecheng Cai %A Shudong Huang %A Jiancheng Lv %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-dai24b %I PMLR %P 9846--9855 %U https://proceedings.mlr.press/v235/dai24b.html %V 235 %X Multi-view clustering has been widely explored for its effectiveness in harmonizing heterogeneity along with consistency in different views of data. Despite the significant progress made by recent works, the performance of most existing methods is heavily reliant on strong priori information regarding the true cluster number $\textit{K}$, which is rarely feasible in real-world scenarios. In this paper, we propose a novel graph-based multi-view clustering algorithm to infer unknown $\textit{K}$ through a graph consistency reward mechanism. To be specific, we evaluate the cluster indicator matrix during each iteration with respect to diverse $\textit{K}$. We formulate the inference process of unknown $\textit{K}$ as a parsimonious reinforcement learning paradigm, where the reward is measured by inter-cluster connectivity. As a result, our approach is capable of independently producing the final clustering result, free from the input of a predefined cluster number. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed approach in comparison to existing state-of-the-art methods.
APA
Dai, H., Liu, Y., Su, P., Cai, H., Huang, S. & Lv, J.. (2024). Multi-View Clustering by Inter-cluster Connectivity Guided Reward. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:9846-9855 Available from https://proceedings.mlr.press/v235/dai24b.html.

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