Multi-label classification with extreme learning machine

Abstract

Extreme learning machine (ELM) is a well-known algorithm for single layer feedforward neural networks (SLFNs) and their learning speed is faster than traditional gradient-based neural networks. However, many of the tasks that ELM focuses on are single-label, where an instance of the input set is associated with one label. This paper proposes a new method for training ELM that will be capable of multi-label classification using the Canonical Correlation Analysis (CCA). The new method is named CCA-ELM. There are 4 steps in the training process: the first step is to compute any correlations between the input features and the set of labels using CCA, the second step maps the input space and label space to the new space, the third step uses ELM to classify and the last step is to map to the original input space. The experimental results show that CCA-ELM can improve ELM for classification on multi-label learning and its recognition performances are better than the other comparative algorithms that use the same standard CCA.