Extreme Learning Machine based weighting for decision rule in Collaborative Representation Classifier

Abstract

Sparse representation based classification (SRC) has been widely used for pattern recognition especially to face recognition due to its robustness to illumination change, noise and occlusion in face images. SRC method emphasizes the role of parsimonious representation in achieving robustness and accurate classification. To enforce sparsity, the linear representation of query sample and training samples is computed using l1-minimization which is complex and time consuming. Recently, many studies have proved the robustness of SRC is achieved by the collaborative representation mechanism and not the l1 sparsity constraint. Thus, the l1-norm based representation in SRC classification framework could be replaced by l2-norm based representation which is computationally more efficient. This type of classification method is called Collaborative representation based classification (CRC) in the literature. In this paper, an output weight computed from extreme learning machine ELM regarded as a class membership is utilized in conjunction with the classification decision in collaborative representation classifier to improve high classification accuracy over various public available face and speech recognition datasets. The role of ELM is to provide the class membership which denotes the nonlinear similarity of the query sample to training samples from each class. Whereas the CRC provides the linear representation of the query sample and training. The collaborative representation from CRC and class membership from ELM are applied in the regularized residual classification decision to classify the query sample. Experimental results prove that the classification accuracy of the proposed algorithm i.e. CRC-ELM is greatly improved the baseline performances.