Bayesian Tensor Models for Multiway Structural Data: A Theoretical Study and Applications
| Project/Area Number |
15K15955
|
|---|
| Research Category |
Grant-in-Aid for Young Scientists (B)
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Statistical science
|
|---|
| Research Institution | Institute of Physical and Chemical Research |
|---|
Principal Investigator |
Zhao Qibin 国立研究開発法人理化学研究所, 脳科学総合研究センター, 研究員 (30599618)
|
|---|
| Project Period (FY) |
2015-04-01 – 2017-03-31
|
| Project Status |
Completed (Fiscal Year 2016)
|
| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2015: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
|
|---|
| Keywords | Tensor Factorization / Missing Data / Tensor Completion / Robust Factorization / Bayesian Inference / Tensor Denoising / Machine Learning / Image Completion / Image Denoising |
|---|
| Outline of Final Research Achievements |
In this project, we study the probabilistic model of tensor factorizations and their applications to EEG signals and computer vision. 1) We proposed a Bayesian robust CP tensor factorization with missing data, which can infer automatically the underlying CP rank and capture outliers effectively from partially observed data without tuning parameters. 2) We proposed a tensor completion method with smoothness constraints over latent factors, which is particularly useful for visual data. 3) We proposed an efficient algorithm for non-negative Tucker decomposition by employing low-rank approximations of the gradient. 4) We proposed a nonlinear tensor partial least squares algorithm for multi-block tensor regression. 5) We applied our proposed methods to EEG artifact removal, image/video denoisiong, and MRI denoising.
|
Report
(3 results)
Research Products
(10 results)
