Multi-instance Multi-label Learning by Extreme Learning Machine

Abstract

Multi-instance Multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy based methods often suffer from some common drawbacks: (1) the user-specific parameter for the number of clusters may incur the effective problem; (2) utilizing SVM as the classifiers builder may bring the high computational cost. In this paper, we propose an algorithm, namely MIML-ELM, to address the problems. To our best knowledge, we are the first utilizing ELM in MIML problem and conducting the comparison of ELM and SVM on MIML. Extensive experiments are conducted on the real datasets and the synthetic datasets. The results show that MIML-ELM tends to achieve better generalization performance at a higher learning speed.