Adaptive Transfer Clustering: A Unified Framework
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915840481296384 |
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| author | Gu, Yuqi Lyu, Zhongyuan Wang, Kaizheng |
| author_facet | Gu, Yuqi Lyu, Zhongyuan Wang, Kaizheng |
| contents | We propose a general transfer learning framework for clustering given a main dataset and an auxiliary one about the same subjects. The two datasets may reflect similar but different latent grouping structures of the subjects. We propose an adaptive transfer clustering (ATC) algorithm that automatically leverages the commonality in the presence of unknown discrepancy, by optimizing an estimated bias-variance decomposition. It applies to a broad class of statistical models including Gaussian mixture models, stochastic block models, and latent class models. A theoretical analysis proves the optimality of ATC under the Gaussian mixture model and explicitly quantifies the benefit of transfer. Extensive simulations and real data experiments confirm our method's effectiveness in various scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21263 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Transfer Clustering: A Unified Framework Gu, Yuqi Lyu, Zhongyuan Wang, Kaizheng Methodology Machine Learning Statistics Theory 62F35, 62C20 We propose a general transfer learning framework for clustering given a main dataset and an auxiliary one about the same subjects. The two datasets may reflect similar but different latent grouping structures of the subjects. We propose an adaptive transfer clustering (ATC) algorithm that automatically leverages the commonality in the presence of unknown discrepancy, by optimizing an estimated bias-variance decomposition. It applies to a broad class of statistical models including Gaussian mixture models, stochastic block models, and latent class models. A theoretical analysis proves the optimality of ATC under the Gaussian mixture model and explicitly quantifies the benefit of transfer. Extensive simulations and real data experiments confirm our method's effectiveness in various scenarios. |
| title | Adaptive Transfer Clustering: A Unified Framework |
| topic | Methodology Machine Learning Statistics Theory 62F35, 62C20 |
| url | https://arxiv.org/abs/2410.21263 |