CTD: 一种快速、准确且可解释的静态和动态张量分解方法
CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions
AI总结：
本文提出CTD方法，用于高效且可解释地进行静态和动态张量分解，通过去除冗余提升准确性和效率，适用于在线环境下的异常检测。
摘要：
如何在高效且直接可解释的方式下发现张量中的模式和异常？如何在在线环境中处理不断到来的张量？张量模式和异常检测是关键问题，应用于安全监控、健康监测、网络安全等领域。标准的PARAFAC和Tucker分解结果不可直接解释。尽管已有基于采样的方法，但需要更快、更高效和更准确。本文提出CTD，一种基于采样的快速、准确且可解释的张量分解方法。CTD-S在准确性上比现有方法高17-83倍，速度和内存效率也分别提升5-86倍和7-12倍。CTD-D是首个可解释的动态张量分解方法，通过利用前一时间步的因素和重新排列操作，使速度提升2-3倍。通过CTD展示了如何在在线分布式拒绝服务（DDoS）攻击检测中有效解释结果。
英文摘要：
How can we find patterns and anomalies in a tensor, or multi-dimensional array, in an efficient and directly interpretable way? How can we do this in an online environment, where a new tensor arrives each time step? Finding patterns and anomalies in a tensor is a crucial problem with many applications, including building safety monitoring, patient health monitoring, cyber security, terrorist detection, and fake user detection in social networks. Standard PARAFAC and Tucker decomposition results are not directly interpretable. Although a few sampling-based methods have previously been proposed towards better interpretability, they need to be made faster, more memory efficient, and more accurate. In this paper, we propose CTD, a fast, accurate, and directly interpretable tensor decomposition method based on sampling. CTD-S, the static version of CTD, provably guarantees a high accuracy that is 17 ~ 83x more accurate than that of the state-of-the-art method. Also, CTD-S is made 5 ~ 86x faster, and 7 ~ 12x more memory-efficient than the state-of-the-art method by removing redundancy. CTD-D, the dynamic version of CTD, is the first interpretable dynamic tensor decomposition method ever proposed. Also, it is made 2 ~ 3x faster than already fast CTD-S by exploiting factors at previous time step and by reordering operations. With CTD, we demonstrate how the results can be effectively interpreted in the online distributed denial of service (DDoS) attack detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
