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J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:p> With the development of machine learning and computer vision, classification technology is becoming increasingly important. Due to the advantage in efficiency and effectiveness, collaborative representation-based classifiers (CRC) have been applied to many practical cognitive fields. In this paper, we propose a new neighborhood prior constrained collaborative representation model for pattern classification. Compared with the naive CRC models which approximate the test sample with all the training data globally, our proposed methods emphasize the guidance of the neighborhood priors in the coding process. Two different kinds of neighbor priors and the models\u2019 weighted extensions are explored from the view of sample representation ability and relationships between the samples. 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