Self-paced hierarchical metric learning (SPHML)

Mohammed Al-taezi, Pengfei Zhu, Qinghua Hu, Yu Wang, Abdulrahman Al-Badwi

Abstract: Metric learning aims to learn a distance to measure the difference between two samples, and it plays an important role in pattern recognition tasks. Most of the existing metric learning methods rely on pairs of samples. However, the importance of sample pairs varies greatly because of possible noise and the difference between samples and the decision boundaries. In this paper, we propose a robust hierarchical metric learning (SPHML) framework based on self-paced learning, which can help gain knowledge about the weights of sample pairs and utilize them in an easy or hard manner. Hierarchical nonlinear functions are learned by back-propagation to map sample pairs into a more discriminative feature space. Experimentally, our method achieves very competitive performance when compared with state-of-the-art methods.