Multi-task clustering ELM for VIS-NIR cross-modal feature learning

Yi Jin, Jie Li, Congyan Lang, Qiuqi Ruan

Abstract: Extreme learning machine (ELM) as a new emergent and efficient machine learning algorithm has shown its good performance in many real regression applications as well as large data classification. In this paper, we propose a new multi-task clustering ELM for cross-modal feature learning. Different to traditional face recognition methods, a coupled cross-modal feature learning based face descriptor is proposed to reduce the cross-modal differences, meanwhile, the multi-task learning is integrated with ELM for cross-modal classification. In this method, the discriminant feature learning is firstly proposed to learn the cross-modality feature representation. Then, common subspace learning based method is utilized to reduce the obtained cross-modality features. Finally, a multi-task clustering based ELM is proposed to improve the recognition accuracy by learning the shared information between tasks. Experiments conducted on two different VIS-NIR face recognition scenarios demonstrate the effectiveness of our proposed approach.