| Paper ID | SPTM-20.6 |
| Paper Title |
LEARNING MIXTURES OF SEPARABLE DICTIONARIES FOR TENSOR DATA: ANALYSIS AND ALGORITHMS |
| Authors |
Mohsen Ghassemi, Zahra Shakeri, Anand Sarwate, Waheed Bajwa, Rutgers University, United States |
| Session | SPTM-20: Signal Processing over Graphs and Sparsity-Aware Signal Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SMDSP-SAP] Sparsity-aware Processing |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning. It proposes learning a mixture of separable dictionaries to better capture the structure of tensor data by generalizing the separable dictionary learning model. Two different approaches for learning mixture of separable dictionaries are explored and sufficient conditions for local identifiability of the underlying dictionary are derived in each case. Moreover, computational algorithms are developed to solve the problem of learning mixture of separable dictionaries in both batch and online settings. Numerical experiments are used to show the usefulness of the proposed model and the efficacy of the developed algorithms. |
