CTD: Fast, accurate, and interpretable method for static and dynamic tensor decompositions

How can we find patterns and anomalies in a tensor, i.e., multi-dimensional array, in an efficient and directly interpretable way? How can we do this in an online environment, where a new tensor arrives at each time step? Finding patterns and anomalies in multi-dimensional data have many important applications, including building safety monitoring, health monitoring, cyber security, terrorist detection, and fake user detection in social networks. Standard tensor decomposition results are not directly interpretable and few methods that propose to increase interpretability need to be made faster, more memory efficient, and more accurate for large and quickly generated data in the online environment. We propose two versions of a fast, accurate, and directly interpretable tensor decomposition method we call CTD that is based on efficient sampling method. First is the static version of CTD, i.e., CTD-S, that provably guarantees up to 11× higher accuracy than that of the state-of-the-art method. Also, CTD-S is made up to 2.3× faster and up to 24× more memory-efficient than the state-of-the-art method by removing redundancy. Second is the dynamic version of CTD, i.e. CTD-D, which is the first interpretable dynamic tensor decomposition method ever proposed. It is also made up to 82× faster than the 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 online distributed denial of service (DDoS) attack detection and online troll detection.