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A new intelligent pattern classifier based on structured sparse representation
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DOI：10.1016/j.compeleceng.2020.106641.png)
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Abstract
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A new intelligent pattern classifier based on structured sparse representation is developed, aiming to utilise the subspace structure to simplify the training process and reduce the required training sample size. It is seen that no mathematical optimization is involved in the training stage. In the developed classifier, the classification is made by selecting the optimal linear subspace that can represent data with the highest probability. A nonlinear mapping is designed to ensure data vectors in the feature space are more likely to live in their own subspaces. The sparse representation vectors of input data can then be computed in terms of a dictionary constructed by membership vectors of these subspaces. After that, the Bayesian inference based decision-making algorithm is implemented to use the statistical information imbedded in sparse representation vectors to classify the data efficiently. Lastly, a self-learning mechanism is developed to adjust the membership vectors of the dictionary to ensure the classifier's response to the subsequent application of similar input patterns will be enhanced. The experiment results show excellent classification performance and strong robustness of proposed intelligent pattern classifier. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Intelligent classifier
Pattern classification
Sparse representation classifier
Random nonlinear mapping
Bayesian inference
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