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Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy
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DOI：10.1109/TIP.2020.3033151.png)
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Abstract
En 中文
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.
Keywords:
Tensors
Covariance matrices
Principal component analysis
Kernel
Linear programming
Robustness
Image reconstruction
Tensor decomposition
generalized correntropy
2DSVD
reconstruction
recognition
clustering
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