A Generalized Kernel Risk Sensitive Loss for Robust Two-Dimensional Singular Value Decomposition

• URL: http://arxiv.org/abs/2005.04671v2
• Date: Sat, 4 Jul 2020 04:20:50 GMT
• Title: A Generalized Kernel Risk Sensitive Loss for Robust Two-Dimensional Singular Value Decomposition
• Authors: Miaohua Zhang, Yongsheng Gao
• Abstract summary: Two-dimensional singular decomposition (2DSVD) has been widely used for image processing tasks, such as image reconstruction, classification, and clustering. Traditional 2DSVD is based on the mean square error (MSE) loss, which is sensitive to outliers. We propose a robustDSVD based on a generalized kernel risk of noise and outliers.
• Score: 11.234115388848283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Two-dimensional singular decomposition (2DSVD) has been widely used for image processing tasks, such as image reconstruction, classification, and clustering. However, traditional 2DSVD algorithm is based on the mean square error (MSE) loss, which is sensitive to outliers. To overcome this problem, we propose a robust 2DSVD framework based on a generalized kernel risk sensitive loss (GKRSL-2DSVD) which is more robust to noise and and outliers. Since the proposed objective function is non-convex, a majorization-minimization algorithm is developed to efficiently solve it with guaranteed convergence. The proposed framework has inherent properties of processing non-centered data, rotational invariant, being easily extended to higher order spaces. Experimental results on public databases demonstrate that the performance of the proposed method on different applications significantly outperforms that of all the benchmarks.

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