Discriminative extreme learning machine with supervised sparsity preserving for image classification

In order to seek non-propagation method to train generalized single-hidden layer feed forward neural networks, extreme learning machine was proposed, which has been proven to be an effective and efficient model for both multi-class classification and regression. Different from most of existing studies which consider extreme learning machine as a classifier, we make improvements on it to let it become a feature extraction model in this paper. Specifically, a discriminative extreme learning machine with supervised sparsity preserving (SPELM) model is proposed. From the hidden layer to output layer, SPELM performs as a subspace learning method by considering the discriminative as well as sparsity information of data. The sparsity information of data is identified by solving a supervised sparse representation objective. Experiments are conducted on four widely used image benchmark data sets and the classification results demonstrate the effectiveness of the proposed SPELM model.