An adaptive kernel sparse representation-based classification

Abstract

In recent years, scholars have attached increasing attention to sparse representation. Based on compressed sensing and machine learning, sparse representation-based classification (SRC) has been extensively in classification. However, SRC is not suitable for samples with non-linear structures which arise in many practical applications. Meanwhile, sparsity is overemphasized by SRC, but the correlation information which is of great importance in classification is overlooked. To address these shortcomings, this study puts forward an adaptive kernel sparse representation-based classification (AKSRC). First, the samples were mapped to a high-dimensional feature space from the original feature space. Second, after selecting a suitable kernel function, a sample is represented as the linear combination of training samples of same class. Further more, the trace norm is adopted in AKSRC which is different from general approaches. It’s adaptive to the structure of dictionary which means that a better linear representation which has the most discriminative samples can be obtained. Therefore, AKSRC has more powerful classification ability. Finally, the advancement and effectiveness of the proposed AKSRC are verified by carrying out experiments on benchmark data sets.