Composing tree graphical models with persistent homology features for clustering mixed-type data

Ni, Xiuyan, Quadrianto, Novi, Wang, Yusu and Chen, Chao (2017) Composing tree graphical models with persistent homology features for clustering mixed-type data. International Conference on Machine Learning (ICML), Sydney, Australia, 6 August 2017 - 11 August 2017. Published in: Proceedings of the 34th International Conference on Machine Learning. 70 2622-2631. PMLR ISSN 1938-7228

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Abstract

Clustering 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.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0276 Mathematical statistics
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Depositing User: Novi Quadrianto
Date Deposited: 14 Jun 2017 07:51
Last Modified: 18 Oct 2017 10:23
URI: http://sro.sussex.ac.uk/id/eprint/68591

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