A hybrid approach combining extreme learning machine and sparse representation

Version 1.2.0.0 (3,09 Mo) par Kai Zhang
A simple but very effective combination of ELM and SRC.
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Mise à jour 21 mai 2015

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By incorporating extreme learning machine (ELM) and sparse representation (SRC) into a unified framework, the proposed hybrid classifier not only has advantage of fast testing (the merit of ELM) but also shows notable classification accuracy (the merit of SRC).
We test it for AR face recognition, and it achieves a high accuracy of 95%, better than ELM (91%) and SRC (93.5%).
The bridge between ELM and SRC is the ELM misclassification metric and adaptive protein classes selection.
For more details, please refer to the paper "Luo M, Zhang K. A hybrid approach combining extreme learning machine and sparse representation for image classification[J]. Engineering Applications of Artificial Intelligence, 2014, 27: 228-235.".
Note that it is an improved version of the above paper.

Citation pour cette source

Kai Zhang (2024). A hybrid approach combining extreme learning machine and sparse representation (https://www.mathworks.com/matlabcentral/fileexchange/50878-a-hybrid-approach-combining-extreme-learning-machine-and-sparse-representation), MATLAB Central File Exchange. Récupéré le .

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1.2.0.0

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