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Ensemble extreme learning machine and sparse representation classification. (English) Zbl 1349.93304

Summary: Extreme Learning Machine (ELM) combining with Sparse Representation Classification (SRC) has been developed for image classification recently. However, employing a single ELM network with random hidden parameters may lead to unstable generalization and data partition performance in ELM-SRC. To alleviate this deficiency, we propose an enhanced ensemble based ELM and SRC algorithm (En-SRC) in this paper. Rather than using the output of a single ELM to decide the threshold for data partition, En-SRC incorporates multiple ensembles to enhance the reliability of the classifier. Different from ELM-SRC, a theoretical analysis on the data partition threshold selection of En-SRC is given. Extension to the ensemble based regularized ELM with SRC (EnR-SRC) is also presented in the paper. Experiments on a number of benchmark classification databases show that the proposed methods win a better classification performance with a lower computational complexity than the ELM-SRC approach.

MSC:

93C95 Application models in control theory
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
68T05 Learning and adaptive systems in artificial intelligence
62H30 Classification and discrimination; cluster analysis (statistical aspects)
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