Sparse tensor factorization is a popular tool in multi-way data analysis and is used in applications such as cybersecurity, recommender systems, and social network analysis. In many of these applications, the tensor is not known a priori and instead arrives in a streaming fashion for a potentially unbounded amount of time. Existing approaches for streaming sparse tensors are not practical for unbounded streaming because they rely on maintaining the full factorization of the data, which grows linearly with time. In this work, we present CP-stream, an algorithm for streaming factorization in the model of the canonical polyadic decomposition which does not grow linearly in time or space, and is thus practical for long-term streaming. Additionally, CP-stream incorporates user-specified constraints such as non-negativity which aid in the stability and interpretability of the factorization. An evaluation of CP-stream demonstrates that it converges faster than state-of-the-art streaming algorithms while achieving lower reconstruction error by an order of magnitude. We also evaluate it on real-world sparse datasets and demonstrate its usability in both network traffic analysis and discussion tracking. Our evaluation uses exclusively public datasets and our source code is released to the public as part of SPLATT, an open source high-performance tensor factorization toolkit.
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Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion
Streaming tensor factorization is a powerful tool for processing high-volume and multi-way temporal data in Internet networks, recommender systems and image/video data analysis. Existing streaming tensor factorization algorithms rely on least-squares data fitting and they do not possess a mechanism for tensor rank determination. This leaves them susceptible to outliers and vulnerable to over-fitting. This paper presents a Bayesian robust streaming tensor factorization model to identify sparse outliers, automatically determine the underlying tensor rank and accurately fit low-rank structure. We implement our model in Matlab and compare it with existing algorithms on tensor datasets generated from dynamic MRI and Internet traffic.
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- Award ID(s):
- 1817037
- NSF-PAR ID:
- 10194819
- Date Published:
- Journal Name:
- IEEE International Conference on Data Mining (ICDM)
- Page Range / eLocation ID:
- 1446 to 1451
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDM.2018.00200
