SIAM Journal on Scientific Computing, Volume 43, Issue 1, Page B55-B81, January 2021.
In this paper, we study 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, and hyperspectral unmixing. Numerical results are shown to illustrate the effectiveness of the proposed algorithm.

中文翻译：

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正交非负塔克分解

SIAM科学计算杂志，第43卷，第1期，第B55-B81页，2021
年1月。在本文中，我们研究非负张量数据并提出正交非负Tucker分解（ONTD）。我们讨论了ONTD的一些性质，并开发了增强拉格朗日函数的凸松弛算法来解决优化问题。给出了算法的收敛性。我们将ONTD应用于来自现实应用程序的图像数据集，包括人脸识别，图像表示和高光谱分解。数值结果表明了该算法的有效性。