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| Автори: | , |
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| Формат: | Preprint |
| Опубліковано: |
2024
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| Предмети: | |
| Онлайн доступ: | https://arxiv.org/abs/2401.16708 |
| Теги: |
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| _version_ | 1866909114339164160 |
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| author | Hsu, Yung-Peng Chen, Hung-Hsuan |
| author_facet | Hsu, Yung-Peng Chen, Hung-Hsuan |
| contents | This paper introduces the multivariate beta mixture model (MBMM), a new probabilistic model for soft clustering. MBMM adapts to diverse cluster shapes because of the flexible probability density function of the multivariate beta distribution. We introduce the properties of MBMM, describe the parameter learning procedure, and present the experimental results, showing that MBMM fits diverse cluster shapes on synthetic and real datasets. The code is released anonymously at https://github.com/hhchen1105/mbmm/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16708 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multivariate Beta Mixture Model: Probabilistic Clustering With Flexible Cluster Shapes Hsu, Yung-Peng Chen, Hung-Hsuan Machine Learning Artificial Intelligence This paper introduces the multivariate beta mixture model (MBMM), a new probabilistic model for soft clustering. MBMM adapts to diverse cluster shapes because of the flexible probability density function of the multivariate beta distribution. We introduce the properties of MBMM, describe the parameter learning procedure, and present the experimental results, showing that MBMM fits diverse cluster shapes on synthetic and real datasets. The code is released anonymously at https://github.com/hhchen1105/mbmm/. |
| title | Multivariate Beta Mixture Model: Probabilistic Clustering With Flexible Cluster Shapes |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.16708 |