Discriminative extreme learning machine with supervised sparsity preserving for image classification

摘要

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. (C) 2017 Elsevier B.V. All rights reserved.