A robust matching pursuit algorithm using information theoretic learning

Highlights：

• A robust matching pursuit algorithm based on a new ITL-Correlation and non-second order kernel minimization method is proposed.

• Different from the current matching pursuit algorithms, which only works under Gaussian conditions, the proposed ITL-Correlation can work under non-Gaussian conditions and is robust against heavy-tailed impulsive noise that is commonly associated with large-amplitude outliers.

• A non-second order loss is proposed to provide more flexibility in controlling the reconstruction error, which performs better in detecting occlusions and outliers in the data.

• A new classifier based on the non-second order statistic measurement is developed to minimize the effect from outliers and non-Gaussian noise for robust classification.

• Experimental results demonstrate the superiority of the proposed method in handling the challenging outlier and occlusion problems against the existing methods.