A Generalized Kernel Risk Sensitive Loss for Robust Two-Dimensional Singular Value Decomposition
This work addresses robustness issues in image processing tasks like reconstruction and classification, but it appears incremental as it modifies an existing method for outlier sensitivity.
The authors tackled the sensitivity of traditional two-dimensional singular value decomposition (2DSVD) to outliers by proposing a robust framework based on a generalized kernel risk sensitive loss (GKRSL-2DSVD), which significantly outperformed benchmarks in experiments on public databases.
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.