The Bayesian streaming tensor decomposition method is a novel method to discover the low-rank approximation of streaming data. However, when the streaming data comes from a high-order tensor, tensor structures of existing Bayesian streaming tensor decomposition algorithms may not be suitable in terms of representation and computation power. In this paper, we present a new Bayesian streaming tensor decomposition method based on tensor train (TT) decomposition. Especially, TT decomposition renders an efficient approach to represent high-order tensors. By exploiting the streaming variational inference (SVI) framework and TT decomposition, we can estimate the latent structure of high-order incomplete noisy streaming tensors. The experiments in synthetic and real-world data show the accuracy of our algorithm compared to the state-of-the-art Bayesian streaming tensor decomposition approaches.

翻译：贝叶斯流的沙子分解法是发现流数据低排序近似值的一种新颖方法。 但是,当流数据来自高阶振动时,现有贝叶斯流的沙子分解算法的强势结构可能不适合代表性和计算能力。 在本文中,我们介绍了一种基于高压列车分解的新的贝叶斯流的沙子分解法。 特别是, TT分解使一种高效的方法能够代表高阶拉子。 通过利用流变异感(SVI)框架和TT解剖,我们可以估计高序的不完全噪音分解变压体的潜在结构。 合成和现实世界数据的实验显示了我们算法与最先进的巴伊斯河流的高压分解法相比的准确性。