有样本缺失的稀疏保持典型相关分析  

Sparsity Preserving Canonical Correlation Analysis with Missing Samples

作　　者：祖辰[1] 张道强[1] 

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京210016

出　　处：《模式识别与人工智能》2014年第2期179-186,共8页Pattern Recognition and Artificial Intelligence

基　　金：高等学校博士学科点专项科研基金项目(No.20123218110009);南京航空航天大学基本科研业务费项目(No.NE2013105)资助

摘　　要：在典型相关分析(CCA)的基础上,通过稀疏保持引入样本的类别信息,利用交叉相关项克服CCA及其推广算法要求不同视图样本必须成对出现的局限,提出一种有监督学习方法——有样本缺失的稀疏保持典型相关分析(SPCCAM).SPCCAM能在训练样本不成对的情况下进行多视图特征融合.在人工数据集、手写体数据集和PIE人脸数据集上的实验结果表明,SPCCAM能有效利用类信息提高分类性能.Based on the canonical correlation analysis （ CCA ） , a supervised learning algorithm, sparsity preserving CCA with missing samples （ SPCCAM ） , is proposed. The class information of samples is introduced by sparsity preserving and cross correlation is used to overcome the limitations of the CCA and its extensions that the paired samples of different views are required. SPCCAM can combine features from different views with unpaired training samples. The experimental results on the artificial dataset, multiple feature database and PIE facial database show that the proposed SPCCAM effectively enhances the classification performance by using class information.

关 键 词：典型相关分析( CCA) 缺失样本 稀疏保持 多视图降维 CANONICAL CORRELATION Analysis( CCA) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]