Traditional tensor decomposition methods, e.g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are sensitive to outliers. To overcome this problem, in this paper we propose a new robust tensor decomposition method using generalized correntropy criterion (Corr-Tensor). A Lagrange multiplier method is used to effectively optimize the generalized correntropy objective function in an iterative manner. The Corr-Tensor can effectively improve the robustness of tensor decomposition with the existence of outliers without introducing any extra computational cost. Experimental results demonstrated that the proposed method significantly reduces the reconstruction error on face reconstruction and improves the accuracies on handwritten digit recognition and facial image clustering.

中文翻译：

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基于广义熵的鲁棒张量分解

传统的张量分解方法（例如，将均方误差最小化的二维主成分分析和二维奇异值分解）对异常值敏感。为了解决这个问题，在本文中，我们提出了一种使用广义熵准则（Corr-Tensor）的新的鲁棒张量分解方法。拉格朗日乘数法用于以迭代方式有效地优化广义的熵目标函数。Corr-Tensor可以在存在异常值的情况下有效地提高张量分解的鲁棒性，而不会引起任何额外的计算成本。