Orthogonal Nonnegative Tucker Decomposition
Machine Learning
2019-10-29 v2 Machine Learning
Abstract
In this paper, we study the nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relaxation algorithm of the augmented Lagrangian function to solve the optimization problem. The convergence of the algorithm is given. We employ ONTD on the image data sets from the real world applications including face recognition, image representation, hyperspectral unmixing. Numerical results are shown to illustrate the effectiveness of the proposed algorithm.
Cite
@article{arxiv.1910.09979,
title = {Orthogonal Nonnegative Tucker Decomposition},
author = {Junjun Pan and Michael K. Ng and Ye Liu and Xiongjun Zhang and Hong Yan},
journal= {arXiv preprint arXiv:1910.09979},
year = {2019}
}