|
|||||
|
|
| 增强组合特征判别性的典型相关分析 | |
| 引用本文： | 周旭东,陈晓红,陈松灿.增强组合特征判别性的典型相关分析[J].模式识别与人工智能,2012,25(2):285-291. |
| 作者姓名： | 周旭东 陈晓红 陈松灿 |
| 作者单位： | 1. 南京航空航天大学 计算机科学与技术学院 南京210016;扬州大学信息工程学院 扬州225009 2. 南京航空航天大学理学院 南京210016 3. 南京航空航天大学 计算机科学与技术学院 南京210016;南京大学计算机软件新技术国家重点实验室 南京210093 |
| 基金项目： | 国家自然科学基金(No.61170151);江苏省自然科学基金(No.BK2011728)资助项目 |
| 摘 要： | 典型相关分析(CCA)在执行分类任务时主要存在如下不足:1)尽管分类时的输入是组合特征,但CCA仅优化组合特征的各组成部分,并未直接优化组合特征本身；2)尽管面对的是分类任务,然而CCA根本无法利用样本的类信息.为弥补CCA的上述不足,文中提出一种监督型降维方法——增强组合特征判别性的典型相关分析(CECCA).CECCA在CCA基础上,通过结合组合特征的判别分析,实现对组合特征相关性与判别性的联合优化,使所抽取特征更适合分类.在人工数据集、多特征手写体数据集和人脸数据集上的实验结果验证该方法的有效性. |
| 关 键 词： | 典型相关分析(CCA) 分类 降维 组合特征 信息融合 |
Combined-Feature-Discriminability Enhanced Canonical Correlation Analysis |
|
| ZHOU Xu-Dong , CHEN Xiao-Hong , CHEN Song-Can.Combined-Feature-Discriminability Enhanced Canonical Correlation Analysis[J].Pattern Recognition and Artificial Intelligence,2012,25(2):285-291. | |
| Authors: | ZHOU Xu-Dong CHEN Xiao-Hong CHEN Song-Can |
| Affiliation: | 1,4 1(College of Computer Science and Technology,Nanjing University of Aeronautics & Astronautics,Nanjing 210016) 2(Information Engineering College,Yangzhou University,Yangzhou 225009) 3(College of Science,Nanjing University of Aeronautics & Astronautics,Nanjing 210016) 4(National Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210093) |
| Abstract: | Canonical Correlation Analysis(CCA) has following two deficiencies in performing classification task:CCA can not directly optimize them but their components,though combined features are the input of the classifier;CCA can not utilize any class information at all,though facing classification task.To overcome these deficiencies,a supervised dimension reduction method named combined-feature-discriminability enhanced canonical correlation analysis(CECCA) is proposed.CECCA is developed through incorporating discriminant analysis of combined features into CCA.Consequently,it optimizes the combined feature correlation and discriminability simultaneously and thus makes the extracted features more suitable for classification.The experimental results on artificial dataset,multiple feature database and facial databases show that the proposed method is effective. |
| Keywords: | Canonical Correlation Analysis(CCA) Classification Dimension Reduction Combined Features Information Fusion |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |