Kernel Extreme Learning Machine for Learning from Label Proportions

Abstract

As far as we know, Inverse Extreme Learning Machine (IELM) is the first work extending ELM to LLP problem. Due to basing on extreme learning machine (ELM), it obtains the fast speed and achieves competitive classification accuracy compared with the existing LLP methods. Kernel extreme learning machine (KELM) generalizes basic ELM to the kernel-based framework. It not only solves the problem that the node number of the hidden layer in basic ELM depends on manual setting, but also presents better generalization ability and stability than basic ELM. However, there is no research based on KELM for LLP. In this paper, we apply KELM and design the novel method LLP-KELM for LLP. The classification accuracy is greatly improved compared with IELM. Lots of numerical experiments manifest the advantages of our novel method.