SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams

• URL: http://arxiv.org/abs/2102.11517v1
• Date: Tue, 23 Feb 2021 06:28:42 GMT
• Title: SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams
• Abstract summary: We propose SLICENSTITCH for continuous CANDECOMP/PARAFAC decomposition. It has numerous time-critical applications, including anomaly detection, recommender systems, and stock market prediction. We show, theoretically and experimentally, that SLICENSTITCH is (1) 'Any time': updating factor immediately without having to wait until the current time period ends, (2) Fast: with constant-time updates up to 759x faster than online methods, and (3) Accurate: with fitness comparable (specifically, 72 160%) to offline methods.
• Score: 22.348422397331984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Consider traffic data (i.e., triplets in the form of source-destination-timestamp) that grow over time. Tensors (i.e., multi-dimensional arrays) with a time mode are widely used for modeling and analyzing such multi-aspect data streams. In such tensors, however, new entries are added only once per period, which is often an hour, a day, or even a year. This discreteness of tensors has limited their usage for real-time applications, where new data should be analyzed instantly as it arrives. How can we analyze time-evolving multi-aspect sparse data 'continuously' using tensors where time is'discrete'? We propose SLICENSTITCH for continuous CANDECOMP/PARAFAC (CP) decomposition, which has numerous time-critical applications, including anomaly detection, recommender systems, and stock market prediction. SLICENSTITCH changes the starting point of each period adaptively, based on the current time, and updates factor matrices (i.e., outputs of CP decomposition) instantly as new data arrives. We show, theoretically and experimentally, that SLICENSTITCH is (1) 'Any time': updating factor matrices immediately without having to wait until the current time period ends, (2) Fast: with constant-time updates up to 759x faster than online methods, and (3) Accurate: with fitness comparable (specifically, 72 ~ 160%) to offline methods.

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