论文标题
强大的二维奇异值分解的广义内核风险敏感损失
A Generalized Kernel Risk Sensitive Loss for Robust Two-Dimensional Singular Value Decomposition
论文作者
论文摘要
二维奇异分解（2DSVD）已被广泛用于图像处理任务，例如图像重建，分类和聚类。但是，传统的2DSVD算法基于均方根误差（MSE）损失，该损失对离群值敏感。为了克服这个问题，我们提出了一个基于广义内核风险敏感损失（GKRSL-2DSVD）的强大2DSVD框架，这对噪声和异常值更为强大。由于所提出的目标函数是非凸，因此开发了一种多数化最小化算法，以有效地解决它。所提出的框架具有处理非中心数据的固有属性，即旋转不变，很容易扩展到高阶空间。公共数据库的实验结果表明，所提出的方法在不同应用程序上的性能大大优于所有基准测试。
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.
