Streaming data recovery via Bayesian tensor train decomposition
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913245616406528 |
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| author | Huang, Yunyu Feng, Yani Liao, Qifeng |
| author_facet | Huang, Yunyu Feng, Yani Liao, Qifeng |
| contents | In this paper, we study a Bayesian tensor train (TT) decomposition method to recover streaming data by approximating the latent structure in high-order streaming data. Drawing on the streaming variational Bayes method, we introduce the TT format into Bayesian tensor decomposition methods for streaming data, and formulate posteriors of TT cores. Thanks to the Bayesian framework of the TT format, the proposed algorithm (SPTT) excels in recovering streaming data with high-order, incomplete, and noisy properties. The experiments in synthetic and real-world datasets show the accuracy of our method compared to state-of-the-art Bayesian tensor decomposition methods for streaming data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_12148 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Streaming data recovery via Bayesian tensor train decomposition Huang, Yunyu Feng, Yani Liao, Qifeng Machine Learning Statistics Theory In this paper, we study a Bayesian tensor train (TT) decomposition method to recover streaming data by approximating the latent structure in high-order streaming data. Drawing on the streaming variational Bayes method, we introduce the TT format into Bayesian tensor decomposition methods for streaming data, and formulate posteriors of TT cores. Thanks to the Bayesian framework of the TT format, the proposed algorithm (SPTT) excels in recovering streaming data with high-order, incomplete, and noisy properties. The experiments in synthetic and real-world datasets show the accuracy of our method compared to state-of-the-art Bayesian tensor decomposition methods for streaming data. |
| title | Streaming data recovery via Bayesian tensor train decomposition |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2302.12148 |