基于广义相关熵的图像鲁棒张量分解
Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy
摘要：
传统的张量分解方法，例如，最小化均方误差的二维的主成分分析和二维的奇异值分解对离群值敏感。为了克服这个问题，本文提出了一种新的鲁棒张量分解方法，采用广义相关熵准则（Corr-Tensor）。采用拉格朗日乘子法对广义相关熵目标函数进行迭代优化。Corr-Tensor方法可以有效地提高张量分解对离群点的鲁棒性，而不需要增加额外的计算量。实验结果表明，该方法显著降低了人脸重建的误差，提高了手写数字识别和人脸图像聚类的准确率。
英文摘要：
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.
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