Name
Title | ||
|---|---|---|
Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion. |
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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Machine Learning | Recommender system,Data mining,Bayesian inference,Tensor,Computer science,Outlier,Temporal database,Sparse matrix,Internet traffic,Bayesian probability |
DocType | Volume | Citations |
Journal | abs/1809.02153 | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cole Hawkins | 1 | 1 | 1.70 |
| Zheng Zhang | 2 | 125 | 12.54 |