| Paper ID | SAM-13.2 |
| Paper Title |
Canonical Polyadic Tensor Decomposition with Low-Rank Factor Matrices |
| Authors |
Anh-Huy Phan, Skolkovo Institute of Science and Technology, Russia; Petr Tichavsky, The Czech Academy of Sciences, Institute of Information Theory and Automation, Czechia; Konstantin Sobolev, Konstantin Sozykin, Dmitry Ermilov, Andrzej Cichocki, Skolkovo Institute of Science and Technology, Russia |
| Session | SAM-13: Multi-Channel Data Fusion and Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Sensor Array and Multichannel Signal Processing: [SAM-TNSR] Tensor-based signal processing for multi-sensor systems |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
This paper proposes a constrained canonical polyadic (CP) tensor decomposition method with low-rank factor matrices. In this way, we allow the CP decomposition with high rank while keeping the number of the model parameters small. First, we propose an algorithm to decompose the tensors into factor matrices of given ranks. Second, we propose an algorithm which can determine the ranks of the factor matrices automatically, such that the fitting error is bounded by a user-selected constant. The algorithms are verified on the decomposition of a tensor of the MNIST hand-written image dataset. |
