论文标题
基于广义correntropy的图像表示的稳健张量分解
Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy
论文作者
论文摘要
传统的张量分解方法，例如，二维主成分分析和二维奇异值分解（最小化平方误差）对离群值敏感。为了克服这个问题，在本文中，我们提出了一种使用广义Correntropy Criterion（Correntor）的新的稳健张量分解方法。 Lagrange乘数方法用于以迭代方式有效地优化广义的CorrentRopy目标函数。 CorrTensor可以有效地提高张量分解的鲁棒性，而在不引入任何额外的计算成本的情况下存在异常值。实验结果表明，所提出的方法大大减少了面部重建的重建误差，并改善了手写数字识别和面部图像聚类的精度。
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.
