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 | ||
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. |