A robust matching pursuit algorithm using information theoretic learning

PATTERN RECOGNITION

卷:107

Article

Zhang, Miaohua¹  Gao, Yongsheng¹  Sun, Changming²  Blumenstein, Michael³ 

[1] Griffith Univ, Sch Engn & Built Environm, Nathan, Qld, Australia

[2] CSIRO Data61, Marsfield, NSW, Australia

[3] Univ Technol Sydney, Fac Engn & IT, Sydney, NSW, Australia

关键词: Orthogonal matching pursuit;    Information theoretic learning;    ITL-Correlation;    Kernel minimization;    Data recovery;    Image reconstruction;    Image classification;   

DOI  :  10.1016/j.patcog.2020.107415

来源: Elsevier

【 摘 要 】

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 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 a 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. Crown Copyright (C) 2020 Published by Elsevier Ltd. All rights reserved.

【 授权许可】

Free