Hybrid classification approach using extreme learning machine and sparse representation classifier with adaptive threshold

Abstract

Here, the authors propose a hybrid classification approach using extreme learning machine (ELM) and sparse representation classifier (SRC) with adaptive threshold, which they called ATELMSRC. ATELMSRC can adaptively adjust the threshold, and make more test images correctly classified by ELM compared with ELMSRC, which not only reduces the classification time greatly but also improves the classification accuracy. In addition, primal augmented Lagrangian method is used in ATELMSRC to speed up the solution of l 1 -norm, which also speeds up the classification process. Experimental results on USPS handwritten digits data set and UMIST face data set show that the total classification time of the authors ATELMSRC is very short for large data sets, only 1/310 of SRC, 1/805 of extended SRC (ESRC), and 1/41 of ELMSRC. Meanwhile, the classification accuracy of the authors’ ATELMSRC is 97.80% on USPS handwritten digits data set, and 99.27% on UMIST face data set, which are higher than those of ELM, SRC, ESRC, ELMSRC etc.