Multi-label Text Categorization Using L21-norm Minimization Extreme Learning Machine

Extreme learning machine (ELM) was extended from the generalized single hidden layer feedforward networks where the input weights of the hidden layer nodes can be assigned randomly. It has been widely used for its much faster learning speed and less manual works. Considering the field of multi-label text classification, in this paper, we propose an ELM based algorithm combined with L-21-norm minimization of the output weights matrix called L-21-norm Minimization ELM, which not only fully inherits the merits of ELM but also facilitates group sparsity and reduces complexity of the learning model. Extensive experiments on several bench-mark data sets show a more desirable performance compared with other common multi-label classification algorithms.