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.

翻译：本文研究了一种贝叶斯张量火车（TT）分解方法，通过逼近高阶流数据中的潜在结构来恢复流数据。借鉴流变分贝叶斯方法，我们将TT格式引入面向流数据的贝叶斯张量分解方法，并推导了TT核心的后验分布。得益于TT格式的贝叶斯框架，所提算法（SPTT）在恢复具有高阶、不完整和噪声特性的流数据方面表现出色。在合成数据集和真实数据集上的实验表明，与现有的面向流数据的贝叶斯张量分解方法相比，本文方法具有更高的准确性。