关键词: 特征提取, 多重集典型相关分析, 集成学习, 模式识别

Abstract: Feature extraction, which has significance to improve the classification performance, is an important problem in pattern recognition. The commonly used feature extraction method includes: Principal Components Analysis, Linear Discriminant Analysis and Canonical Correlations Analysis and so on. Multi-set Canonical Correlations Analysis, which is based on traditional CCA, uses multiple dataset to do feature extraction. This paper proposes a new method EMCCA which combines MCCA with Ensemble learning, divides the sample dataset into some sample datasets and does feature extraction on these datasets. The experimental results on UCI standard sets show that: compared with traditional PCA, CCA, EMCCA has better performance in feature extraction and classification with the help of ensemble learning.

Key words: feature extraction, multi-set canonical correlations analysis, ensemble learning, pattern recognition