Sparse Coding Extreme Learning Machine for Classification

Abstract

As one of supervised learning algorithms, extreme learning machine (ELM) has been proposed for single-hidden-layer feedforward neural networks (SLFN) and shown great generalization performance. ELM randomly assigns the weights and biases between the input and hidden layers and trains 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 the output of each layer is a type of sparse codings. However, it is difficult to accommodate the lateral inhibition by directly using random feature mapping in ELM. Therefore, this paper proposes a sparse coding ELM (ScELM) algorithm, which can map the input feature vector into a sparse representation such that the mapped feature is sparse. In this proposed ScELM algorithm, an unsupervised way is used for sparse coding in the sense that dictionary is randomly assigned rather than learned. Gradient projection (GP) based method is used for the sparse coding. The output weights are trained in the same supervised way which ELM presents. Experimental results on benchmark databases have shown that this proposed ScELM algorithm can outperform other state-of-the art methods in terms of classification accuracy.