A Robust Matching Pursuit Algorithm Using Information Theoretic Learning
This work addresses robustness in sparse representation for applications like image processing, but it is incremental as it modifies an existing method.
The paper tackled the problem of orthogonal matching pursuit (OMP) algorithms being suboptimal with non-Gaussian noises or outliers by developing a new OMP algorithm based on information theoretic learning, which demonstrated superiority in data recovery, image reconstruction, and classification on simulated and real-world data.
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.