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- W2890831639 abstract "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." @default.
- W2890831639 created "2018-09-27" @default.
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- W2890831639 date "2018-11-01" @default.
- W2890831639 modified "2023-09-25" @default.
- W2890831639 title "Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion" @default.
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- W2890831639 doi "https://doi.org/10.1109/icdm.2018.00200" @default.
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