Streaming data recovery via Bayesian tensor train decomposition

Yunyu Huang; Yani Feng; Qifeng Liao

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arXiv · 2302.12148

Streaming data recovery via Bayesian tensor train decomposition

Yunyu Huang·Yani Feng·Qifeng Liao

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Abstract

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.

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BibTeX RIS

Yunyu Huang, Yani Feng, Qifeng Liao. 2023-02-23. Streaming data recovery via Bayesian tensor train decomposition. https://arxiv.org/abs/2302.12148

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