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Composing tree graphical models with persistent homology features for clustering mixed-type data
conference contribution
posted on 2023-06-09, 06:42 authored by Xiuyan Ni, Novi QuadriantoNovi Quadrianto, Yusu Wang, Chao ChenClustering data with both continuous and discrete attributes is a challenging task. Existing methods often lack a principled probabilistic formulation. In this paper, we propose a clustering method based on a tree-structured graphical model to describe the generation process of mixed-type data. Our tree-structured model factorizes into a product of pairwise interactions, and thus localizes the interaction between feature variables of different types. To provide a robust clustering method based on the tree-model, we adopt a topographical view and compute peaks of the density function and their attractive basins for clustering. Furthermore, we leverage the theory from topology data analysis to adaptively merge trivial peaks into large ones in order to achieve meaningful clusterings. Our method outperforms state-of-the-art methods on mixed-type data.
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Publication status
- Published
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- Published version
Journal
Proceedings of the 34th International Conference on Machine LearningISSN
1938-7228Publisher
PMLRPublisher URL
Volume
70Page range
2622-2631Event name
International Conference on Machine Learning (ICML)Event location
Sydney, AustraliaEvent type
conferenceEvent date
6 - 11 August 2017Series
Proceedings on Machine Learning ResearchDepartment affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2017-06-14First Compliant Deposit (FCD) Date
2017-06-14Usage metrics
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