Current orthogonal matching pursuit (OMP) algorithms calculate the correlation between two vectors using the inner product operation and minimize the mean square error, which are both suboptimal when there are non-Gaussian noises or outliers in the observation data. To overcome these problems, a new OMP algorithm is developed based on the information theoretic learning (ITL), which is built on the following new techniques: (1) an ITL-based correlation (ITL-Correlation) is developed as a new similarity measure which can better exploit higher-order statistics of the data, and is robust against many different types of noise and outliers in a sparse representation framework; (2) a non-second order statistic measurement and minimization method is developed to improve the robustness of OMP by overcoming the limitation of Gaussianity inherent in cost function based on second-order moments. The experimental results on both simulated and real-world data consistently demonstrate the superiority of the proposed OMP algorithm in data recovery, image reconstruction, and classification.

中文翻译：

---

使用信息论学习的稳健匹配追踪算法

当前的正交匹配追踪 (OMP) 算法使用内积运算计算两个向量之间的相关性并最小化均方误差，当观测数据中存在非高斯噪声或异常值时，这两种方法都是次优的。为了克服这些问题，基于信息理论学习 (ITL) 开发了一种新的 OMP 算法，该算法建立在以下新技术的基础上： (1) 基于 ITL 的相关性 (ITL-Correlation) 被开发为一种新的相似性度量它可以更好地利用数据的高阶统计数据，并且对稀疏表示框架中的许多不同类型的噪声和异常值具有鲁棒性；(2) 开发了一种非二阶统计量测量和最小化方法，通过克服基于二阶矩的成本函数固有的高斯性限制来提高 OMP 的鲁棒性。模拟数据和真实数据的实验结果一致证明了所提出的 OMP 算法在数据恢复、图像重建和分类方面的优越性。