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In the sparse representation-based classification (SRC), the object recognition procedure depends on local sparsity identification from sparse coding coefficients, where many existing SRC methods have focused on the local sparsity and the samples correlation to improve the classifier performance. However, the coefficients often do not accurately represent the local sparsity due to several factors that affect the data acquisition process such as noise, blurring, and downsampling. Therefore, thisdoi:10.1109/access.2019.2890845 fatcat:auw6e2xzozdl5oa6d77nwd76d4