Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2019
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| _version_ | 1866909217703591936 |
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| author | Sochaniwsky, Alexa A. Gallaugher, Michael P. B. Tang, Yang McNicholas, Paul D. |
| author_facet | Sochaniwsky, Alexa A. Gallaugher, Michael P. B. Tang, Yang McNicholas, Paul D. |
| contents | Robust clustering of high-dimensional data is an important topic because clusters in real datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based clustering often fail for high dimensional data, e.g., due to the number of free covariance parameters. A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed. This parameterization includes a penalty term in the likelihood. An analytically feasible expectation-maximization algorithm is developed by placing a gamma-lasso penalty constraining the concentration matrix. The proposed methodology is investigated through simulation studies and illustrated using two real datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1903_05054 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions Sochaniwsky, Alexa A. Gallaugher, Michael P. B. Tang, Yang McNicholas, Paul D. Methodology Machine Learning Robust clustering of high-dimensional data is an important topic because clusters in real datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based clustering often fail for high dimensional data, e.g., due to the number of free covariance parameters. A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed. This parameterization includes a penalty term in the likelihood. An analytically feasible expectation-maximization algorithm is developed by placing a gamma-lasso penalty constraining the concentration matrix. The proposed methodology is investigated through simulation studies and illustrated using two real datasets. |
| title | Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/1903.05054 |