Multi-label Learning Based on Kernel Extreme Learning Machine

Abstract

In recent years, with the increase of data scale, multi-label learning with large scale class labels has turned out to be the research hotspots. Due to the huge solution space, the problem becomes more complex. Therefore, we propose a multi-label algorithm based on kernel learning machine in this paper. Besides, the Cholesky matrix decomposition inverse method is adopted to calculate the network output weight of the kernel extreme learning machine. In particular, in terms of large matrix inverse problem, the large matrix is divided into small matrices for parallel computation through using matrix block method. Compared with several state-of -the-art algorithms on several benchmark data sets, results of the experiments show that the proposed algorithm makes a better performance with large scale class labels.