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Objective Sparse representation is a popular topic in computer vision and pattern recognition. This process first expresses the test sample as a linear combination of training samples. Sparse representation also determines the class that minimizes deviation for classification purposes. Recent advances in representation-based classification methods show that collaborative representation, ratlier than sparsity, improves face recognition accuracy. Unlike sparse representation, collaborative representation is computationally efficient and recognizes faces well. However, this performance degrades sharply if the training samples are corrupted by noise. This noise is very common in practical applications and causes side effects that destabilize classification results. Collaborative representation also ignores data locality, which is important. To address these two problems, a new algorithm for locality-constrained collaborative representation classification is proposed in this paper for robust categorization. Method Singular value decomposition is performed to remove noise in the training samples, and the training samples are approximated as the clean training samples. Local similarity is employed to maintain the simi-larity between the test sample and its adjacent training samples. Locality is an important characteristic in the reduction and classification of dimensionality because locality results in sparsity but not vice versa. Result The proposed algorithm can obtain a closed-formsolution through collaborative representation and avoid the dilemma of expensive computations in sparse representation. This algorithm also considers local similarity. Experiments are conducted on ORL, Extended YALEB, and PIE face databases. Results demonstrated that the obtained coefficients display much discriminative power and that the proposed algorithm performs well. Moreover, recognition rates reach 91. 4% , 93. 8% , and 93. 2% respectively. Conclusion The proposed algorithm simply and feasibly reduces the effect of noise in the training samples and obtains "clean", samples for representation-based classification. The proposed method measures dissimilarity by applying locality. This technique also suppresses the corresponding weight coefficient for distant samples while emphasizing the role of samples that are similar to the test sample in the training set. The test samples are represented as well. Experimental results demonstrate the feasibility and effectiveness of the algorithm. Thus, this method is a new technique for representation-based classification, such as face recognition, in that the side effects of noise on classification can be eliminated. This elimination is almost impossible for conventional methods. [ABSTRACT FROM AUTHOR]