Effective discriminative TCM-KNN for incremental learning
Xiaohua Huang, Wenming Zheng · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Incremental learning is an efficient scheme for reducing computational complexity of batch learning. Label information in each update is helpful to update discriminative model in incremental learning. However, the procedure of labeling samples is always a time-consuming and tedious task. In this paper, we propose two labeling algorithms for unknown samples, one is discriminative Transductive Confidence Machine for K-Nearest Neighbor (TCM-KNN), the other is its improved algorithm for choosing good quality discriminative samples and enhancing the performance of the procedure of labeling samples; and then these methods is applied in the incremental learning[2] before updating model. Experiment on PIE database has been carried out for comparing their recognition rate and complexity. Extensive experimental results show that the proposed method for incremental learning is more robust and effective than batch learning.