Streaming Nonlinear Bayesian Tensor Decomposition
Streaming Nonlinear Bayesian Tensor Decomposition
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发表时间：
2020
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通讯作者：
Zhimeng Pan;Z. Wang;Shandian Zhe
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作者：
Zhimeng Pan;Z. Wang;Shandian Zhe
尽管基于高斯工艺（GP）的最新非线性张量分解模型缺乏处理流媒体数据的有效方法，这对于许多应用程序很重要解决这个问题，我们提出了在接收新的张量的条目时，可以进行非线性贝叶斯张量分解（SNBTD），可以进行高质量的，封闭形式和无迭代的更新，我们使用随机的傅立叶范围。近似所有数据具有单个因素的流批中，我们使用条件矩匹配和泰勒的近似值来实现效率，分析因子计算。
Despite the success of the recent nonlinear tensor decomposition models based on Gaussian processes (GPs), they lack an effective way to deal with streaming data, which are important for many applications. Using the standard streaming variational Bayes framework or the recent streaming sparse GP approximations will lead to intractable model evidence lower bounds; although we can use stochastic gradient descent for incremental updates, they are unreliable and often yield poor estimations. To address this problem, we propose Streaming Nonlinear Bayesian Tensor Decomposition (SNBTD) that can conduct high-quality, closed-form and iteration-free updates upon receiving new tensor entries. Specifically, we use random Fourier features to build a sparse spectrum GP decomposition model to dispense with complex kernel/matrix operations and to ease posterior inference. We then extend the assumed-density-filtering framework by approximating all the data likelihoods in a streaming batch with a single factor to perform one-shot updates. We use conditional moment matching and Taylor approximations to fulfill efficient, analytical factor calculation. We show the advantage of our method on four real-world applications.