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Proceedings Paper

Effective discriminative TCM-KNN for incremental learning
Author(s): Xiaohua Huang; Wenming Zheng
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Paper 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.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961U (30 October 2009); doi: 10.1117/12.832567
Show Author Affiliations
Xiaohua Huang, Southeast Univ. (China)
Wenming Zheng, Southeast Univ. (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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