论文标题
时间感知的张量分解缺失进入预测
Time-Aware Tensor Decomposition for Missing Entry Prediction
论文作者
论文摘要
鉴于有时间变化的张量，缺少条目，我们如何有效地将其分解以准确预测缺失的条目？张量分解已被广泛用于分析各种多维现实世界数据。但是，现有的张量分解模型无视张量分解的时间属性，而大多数现实世界数据与时间密切相关。此外，由于时间切片的稀疏性，它们无法解决准确性降解。如何利用时间特性进行张量分解并考虑时间切片的稀疏性仍然无法解决的基本问题。在本文中，我们提出了TATD（时间感知张量分解），这是一种新型的张量分解方法，用于现实世界中的时间张量。 TATD旨在利用现实世界中时间张量的时间依赖性和时变稀疏性。我们建议使用高斯内核进行新的平滑正则化，以建模时间依赖性。此外，我们通过考虑时变稀疏性来提高TATD的性能。我们设计了一种交替的优化方案，适用于我们的平滑正规化。广泛的实验表明，TATD提供了分解时间张量的最新精度。
Given a time-evolving tensor with missing entries, how can we effectively factorize it for precisely predicting the missing entries? Tensor factorization has been extensively utilized for analyzing various multi-dimensional real-world data. However, existing models for tensor factorization have disregarded the temporal property for tensor factorization while most real-world data are closely related to time. Moreover, they do not address accuracy degradation due to the sparsity of time slices. The essential problems of how to exploit the temporal property for tensor decomposition and consider the sparsity of time slices remain unresolved. In this paper, we propose TATD (Time-Aware Tensor Decomposition), a novel tensor decomposition method for real-world temporal tensors. TATD is designed to exploit temporal dependency and time-varying sparsity of real-world temporal tensors. We propose a new smoothing regularization with Gaussian kernel for modeling time dependency. Moreover, we improve the performance of TATD by considering time-varying sparsity. We design an alternating optimization scheme suitable for temporal tensor factorization with our smoothing regularization. Extensive experiments show that TATD provides the state-of-the-art accuracy for decomposing temporal tensors.
