Effective discriminative TCM-KNN for incremental learning

Xiaohua Huang; Wenming Zheng

doi:10.1117/12.832567

30 October 2009 Effective discriminative TCM-KNN for incremental learning

Xiaohua Huang, Wenming Zheng

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74961U (2009) https://doi.org/10.1117/12.832567
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

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.

Citation Download Citation

Xiaohua Huang and Wenming Zheng "Effective discriminative TCM-KNN for incremental learning", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961U (30 October 2009); https://doi.org/10.1117/12.832567

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KEYWORDS

Databases

Error analysis

Machine learning

Detection and tracking algorithms

Pattern recognition

Statistical modeling

Performance modeling

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