Sparse coding extreme learning machine for classification

As one of supervised learning algorithms, extreme learning machine (ELM) has been proposed for training single-hidden-layer feedforward neural networks and shown great generalization performance. ELM randomly assigns the weights and biases between input and hidden layers and only learns the weights between hidden and output layers. Physiological research has shown that neurons at the same layer are laterally inhibited to each other such that outputs of each layer are sparse. However, it is difficult for ELM to accommodate the lateral inhibition by directly using random feature mapping. Therefore, this paper proposes a sparse coding ELM (ScELM) algorithm, which can map the input feature vector into a sparse representation. In this proposed ScELM algorithm, an unsupervised way is used for sparse coding and dictionary is randomly assigned rather than learned. Gradient projection based method is used for the sparse coding. The output weights are trained through the same supervised way as ELM. Experimental results on the benchmark datasets have shown that this proposed ScELM algorithm can outperform other state-of-the-art methods in terms of classification accuracy. (C) 2017 Elsevier B.V. All rights reserved.