论文标题
使用信息理论学习的强大匹配追求算法
A Robust Matching Pursuit Algorithm Using Information Theoretic Learning
论文作者
论文摘要
当前的正交匹配追踪（OMP）算法使用内部产品操作计算两个向量之间的相关性，并最大程度地减少均方误差，当观测数据中存在非高斯噪音或异常值时，它们都是次优的。 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）开发了一种非秒统计统计测量和最小化方法，以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。对模拟数据和现实世界数据的实验结果始终证明了在数据恢复，图像重建和分类中提出的OMP算法的优越性。
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.
