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arXiv · 1910.09979

Orthogonal Nonnegative Tucker Decomposition

Junjun Pan·Michael K. Ng·Ye Liu·Xiongjun Zhang·Hong Yan
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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.

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Junjun Pan, Michael K. Ng, Ye Liu, Xiongjun Zhang, Hong Yan. 2019-10-21. Orthogonal Nonnegative Tucker Decomposition. https://arxiv.org/abs/1910.09979

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