Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion
This work addresses robustness and rank determination in streaming tensor factorization for applications like Internet networks and medical imaging, representing an incremental improvement over existing methods.
The paper tackled the problem of streaming tensor factorization being susceptible to outliers and over-fitting by proposing a Bayesian robust model that identifies sparse outliers, automatically determines tensor rank, and fits low-rank structure, achieving improved accuracy on dynamic MRI and Internet traffic datasets.
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.