Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909131853529088 |
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| author | Do, Dat Do, Linh McKinley, Scott A. Terhorst, Jonathan Nguyen, XuanLong |
| author_facet | Do, Dat Do, Linh McKinley, Scott A. Terhorst, Jonathan Nguyen, XuanLong |
| contents | We present a new way to summarize and select mixture models via the hierarchical clustering tree (dendrogram) constructed from an overfitted latent mixing measure. Our proposed method bridges agglomerative hierarchical clustering and mixture modeling. The dendrogram's construction is derived from the theory of convergence of the mixing measures, and as a result, we can both consistently select the true number of mixing components and obtain the pointwise optimal convergence rate for parameter estimation from the tree, even when the model parameters are only weakly identifiable. In theory, it explicates the choice of the optimal number of clusters in hierarchical clustering. In practice, the dendrogram reveals more information on the hierarchy of subpopulations compared to traditional ways of summarizing mixture models. Several simulation studies are carried out to support our theory. We also illustrate the methodology with an application to single-cell RNA sequence analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01684 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models Do, Dat Do, Linh McKinley, Scott A. Terhorst, Jonathan Nguyen, XuanLong Methodology Machine Learning We present a new way to summarize and select mixture models via the hierarchical clustering tree (dendrogram) constructed from an overfitted latent mixing measure. Our proposed method bridges agglomerative hierarchical clustering and mixture modeling. The dendrogram's construction is derived from the theory of convergence of the mixing measures, and as a result, we can both consistently select the true number of mixing components and obtain the pointwise optimal convergence rate for parameter estimation from the tree, even when the model parameters are only weakly identifiable. In theory, it explicates the choice of the optimal number of clusters in hierarchical clustering. In practice, the dendrogram reveals more information on the hierarchy of subpopulations compared to traditional ways of summarizing mixture models. Several simulation studies are carried out to support our theory. We also illustrate the methodology with an application to single-cell RNA sequence analysis. |
| title | Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2403.01684 |