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 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.