| Paper ID | MLSP-7.1 |
| Paper Title |
SPEEDING UP OF KERNEL-BASED LEARNING FOR HIGH-ORDER TENSORS |
| Authors |
Ouafae Karmouda, Jeremie Boulanger, Remy Boyer, University of Lille, France |
| Session | MLSP-7: Tensor Signal Processing |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-TNSR] Tensor-based signal processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Supervised learning is a major task to classify datasets. In our context, we are interested into classification from high-order tensors datasets. The "curse of dimensionality" states that the complexities in terms of storage and computation grow exponentially with the order. As a consequence, the method from the state-of-art based on the Higher-Order SVD (HOSVD) works well but suffers from severe limitation in terms of complexities. In this work, we propose a fast Grassmannian kernel-based method for high-order tensor learning based on the equivalence between the Tucker and the tensor-train decompositions. Our solution is linked to the tensor network, where the aim is to break the initial high-order tensor into a collection of low-order tensors (at most 3-order). We show on several real datasets that the proposed method reaches a similar accuracy classification rate as the Grassmannian kernel-based method based on the HOSVD but for a much lower complexity. |
