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Tensor-Train-Based High-Order Dominant Eigen Decomposition for Multimodal Prediction Services
IEEE Transactions on Engineering Management ( IF 4.6 ) Pub Date : 2021-02-01 , DOI: 10.1109/tem.2019.2912928
Huazhong Liu , Laurence Tianruo Yang , Jihong Ding , Yimu Guo , Stephen S. Yau

By leveraging neoteric analytical techniques associated with big data, numerous new data-focused computation and service models have flourished in service computing systems. Accurate future predictions based on tensor-based multivariate Markov models can vigorously support enterprise decisions. However, the computation efficiency and quick response of tensor-based multimodal prediction approach are seriously restricted by the curse of dimensionality arising from high-order tensor. Therefore, to alleviate the problem, this paper focuses on proposing a tensor-train (TT)-based computation approach with its scalable implementation for high-order dominant eigen decomposition (HODED) in multivariate Markov models. First, we present a TT-based Einstein product directly based on decomposed TT cores and guarantee that the result remains TT format. Then, we put forward a scalable implementation for TT-based Einstein product in a distributed or parallel manner. Afterwards, we propose a scalable TT-based HODED (TT-HODED) algorithm and a multimodal accurate prediction algorithm. Furthermore, a TT-based big data processing and services framework is presented to provide accurate proactive services. Experimental results based on real-world GPS trajectory dataset demonstrate that TT-HODED algorithm can significantly improve the computation efficiency and reduce the running memory on the premise of guaranteeing the almost consistent prediction accuracy compared to the original HODED algorithm.

中文翻译:

用于多模态预测服务的基于张量训练的高阶显性特征分解

通过利用与大数据相关的近代分析技术,许多新的以数据为中心的计算和服务模型在服务计算系统中蓬勃发展。基于张量的多元马尔可夫模型的准确未来预测可以有力地支持企业决策。然而,基于张量的多模态预测方法的计算效率和快速响应受到高阶张量引起的维数灾难的严重限制。因此,为了缓解这个问题,本文重点提出了一种基于张量训练 (TT) 的计算方法,其可扩展实现多元马尔可夫模型中的高阶显性特征分解 (HODED)。首先,我们直接基于分解的 TT 核心提出基于 TT 的爱因斯坦产品,并保证结果保持 TT 格式。然后,我们以分布式或并行方式为基于 TT 的 Einstein 产品提出了可扩展的实现。之后,我们提出了一种可扩展的基于 TT 的 HODED(TT-HODED)算法和一种多模态准确预测算法。此外,提出了基于 TT 的大数据处理和服务框架,以提供准确的主动服务。基于真实世界GPS轨迹数据集的实验结果表明,与原始HODED算法相比,TT-HODED算法在保证几乎一致的预测精度的前提下,可以显着提高计算效率并减少运行内存。我们提出了一种可扩展的基于 TT 的 HODED(TT-HODED)算法和一种多模态准确预测算法。此外,提出了基于 TT 的大数据处理和服务框架,以提供准确的主动服务。基于真实世界GPS轨迹数据集的实验结果表明,与原始HODED算法相比,TT-HODED算法在保证几乎一致的预测精度的前提下,可以显着提高计算效率并减少运行内存。我们提出了一种可扩展的基于 TT 的 HODED(TT-HODED)算法和一种多模态准确预测算法。此外,提出了基于 TT 的大数据处理和服务框架,以提供准确的主动服务。基于真实世界GPS轨迹数据集的实验结果表明,与原始HODED算法相比,TT-HODED算法在保证几乎一致的预测精度的前提下,可以显着提高计算效率并减少运行内存。
更新日期:2021-02-01
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