An Ensemble Learning Method Based on CCA with Pairwise Constraints
Zhang Dao · 2012
The diversity among base classifiers is crucial for ensemble learning,and intuitively resampling pairwise constraints get better diversity than resampling instances. The supervision information in the form of pairwise constraints is introduced for feature extraction of samples to generate new training data based on canonical correlation analysis ( CCA) . In this algorithm,the spirit of ensemble learning is embodied in the way to select constraints. The constraints are resampled randomly to get the diverse base classifiers on multiview data. The experiments are carried out on multiple feature database and Yale and AR facial databases,and the results show that the proposed ensemble method achieves better performance than the conventional ensemble learning methods.