Applying Regularization Least Squares Canonical Correlation Analysis in Extreme Learning Machine for Multi-label Classification Problems

Abstract

Multi-label classification is a type of classification where each instance is associated with a set of labels. Many methods such as BP-MLL, rank-SVM, and MLRBF have been proposed for multi-label classification but their learning abilities are too slow. Extreme Learning Machine (ELM) is a well known algorithm for SLFNs that can learn faster than the traditional gradient-base neural networks and it also provides better generalization performance. However, the classification performance of ELM involving multi-label classification may not be good enough despite its advantage in fast training. Therefore, this paper proposes two multi-label classification approaches in ELM. The first approach uses the 1-norm regularized Least-square for Canonical Correlation Analysis (1-norm LSCCA) to obtain the projection vectors, which in turn uses the vectors to provide the new information. Then, ELM is then used to learn this new information in the new space. The second approach applies the ensemble method to the first approach to reduce the random effects of ELM. The experimental results show that the two proposed methods can improve the performance of ELM in multi-label classification and are also faster than the previous multi-label classification methods.