A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912108437831680 |
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| author | Dong, Yiwei Ye, Shaoxin Cao, Yuwen Han, Qiyu Xu, Hongteng Yang, Hanfang |
| author_facet | Dong, Yiwei Ye, Shaoxin Cao, Yuwen Han, Qiyu Xu, Hongteng Yang, Hanfang |
| contents | Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner. Mixture models of temporal point processes have been proposed to solve this problem, but they often suffer from overfitting, leading to excessive cluster generation with a lack of diversity. To overcome these limitations, we propose a Bayesian mixture model of Temporal Point Processes with Determinantal Point Process prior (TP$^2$DP$^2$) and accordingly an efficient posterior inference algorithm based on conditional Gibbs sampling. Our work provides a flexible learning framework for event sequence clustering, enabling automatic identification of the potential number of clusters and accurate grouping of sequences with similar features. It is applicable to a wide range of parametric temporal point processes, including neural network-based models. Experimental results on both synthetic and real-world data suggest that our framework could produce moderately fewer yet more diverse mixture components, and achieve outstanding results across multiple evaluation metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04397 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior Dong, Yiwei Ye, Shaoxin Cao, Yuwen Han, Qiyu Xu, Hongteng Yang, Hanfang Machine Learning Artificial Intelligence Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner. Mixture models of temporal point processes have been proposed to solve this problem, but they often suffer from overfitting, leading to excessive cluster generation with a lack of diversity. To overcome these limitations, we propose a Bayesian mixture model of Temporal Point Processes with Determinantal Point Process prior (TP$^2$DP$^2$) and accordingly an efficient posterior inference algorithm based on conditional Gibbs sampling. Our work provides a flexible learning framework for event sequence clustering, enabling automatic identification of the potential number of clusters and accurate grouping of sequences with similar features. It is applicable to a wide range of parametric temporal point processes, including neural network-based models. Experimental results on both synthetic and real-world data suggest that our framework could produce moderately fewer yet more diverse mixture components, and achieve outstanding results across multiple evaluation metrics. |
| title | A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.04397 |