计算机工程与应用2017，Vol.53Issue(6)：162-168,173,8.DOI:10.3778/j.issn.1002-8331.1510-0147
基于集成学习的多重集典型相关分析方法
Multi-set canonical correlations analysis based on ensemble learning
摘要
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.关键词
特征提取/多重集典型相关分析/集成学习/模式识别Key words
feature extraction/multi-set canonical correlations analysis/ensemble learning/pattern recognition分类
信息技术与安全科学引用本文复制引用
邱爱昆,朱嘉钢..基于集成学习的多重集典型相关分析方法[J].计算机工程与应用,2017,53(6):162-168,173,8.基金项目
江苏省产学研项目(No.BY2013015-40). （No.BY2013015-40）
