CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions

TL;DR

CTD is a novel tensor decomposition method that is fast, accurate, and interpretable, suitable for online detection of patterns and anomalies in multi-dimensional data across various applications.

Contribution

Introduces CTD, a sampling-based tensor decomposition approach that is faster, more accurate, and interpretable, including the first dynamic version for real-time analysis.

Findings

CTD-S outperforms state-of-the-art in accuracy by 17-83x.

CTD-S is 5-86x faster and more memory-efficient.

CTD-D enables real-time dynamic tensor analysis, 2-3x faster than CTD-S.

Abstract

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.