[1]倪怀发,沈肖波,孙权森.基于低秩分解的鲁棒典型相关分析[J].智能系统学报,2017,12(4):491-497.[doi:10.11992/tis.201607024]
NI Huaifa,SHEN Xiaobo,SUN Quansen.Robust canonical correlation analysis based onlow rank decomposition[J].CAAI Transactions on Intelligent Systems,2017,12(4):491-497.[doi:10.11992/tis.201607024]
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NI Huaifa,SHEN Xiaobo,SUN Quansen.Robust canonical correlation analysis based onlow rank decomposition[J].CAAI Transactions on Intelligent Systems,2017,12(4):491-497.[doi:10.11992/tis.201607024]
基于低秩分解的鲁棒典型相关分析
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第4期
页码:
491-497
栏目:
学术论文—人工智能基础
出版日期:
2017-08-25
- Title:
- Robust canonical correlation analysis based onlow rank decomposition
- Keywords:
- Pattern recognition; feature extraction; data dimensionality reduction; canonical correlation analysis; low rank representation; low rank decomposition; low rank component; noise component
- 分类号:
- TP391
- 摘要:
- 典型相关分析（CCA）是一种经典的多特征提取算法，它能够有效地抽取两组特征之间的相关性，现已被广泛应用于模式识别。在含噪声数据情况下，CCA的特征表示性能受到限制。为了使CCA更好地处理含噪声数据，提出一种基于低秩分解的典型相关分析算法——鲁棒典型相关分析（robust canonical correlation analysis，RbCCA）。RbCCA首先对特征集进行低秩分解，得到低秩分量和噪声分量，以此分别构建对应的协方差矩阵。通过最大化低秩分量的相关性，同时最小化噪声分量的相关性来建立判别准则函数，进而求取鉴别投影矢量。在MFEAT手写体数据库、ORL和Yale人脸数据中的实验结果表明，在包含噪声的情况下，RbCCA的识别效果优于现有的典型相关分析方法。
- Abstract:
- Canonical correlation analysis (CCA) is a popular multi-feature extraction method, which can effectively explore the correlations between two sets of features. Up to now, CCA has been widely used in pattern recognition, however it has limited feature extraction power for large noisy data. For CCA to deal better with noisy data, a new method, robust canonical correlation analysis (RbCCA), based on low rank decomposition, is proposed. RbCCA first decomposes features using low rank decomposition to get the low rank and noisy components, then it constructs new covariance matrices based on these two components. A discriminative criteria function is further established to obtain discriminative projections by maximizing the correlations of the low rank component and minimizing the correlations of the noisy component. Experimental results on a MFEAT handwritten dataset, and ORL and Yale face datasets show that RbCCA can achieve higher recognition rates than existing CCA methods, especially in noisy settings.
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备注/Memo
收稿日期:2016-07-24。
基金项目:国家自然科学基金项目（61273251）.
作者简介:倪怀发,男,1990年生,硕士研究生,主要研究方向为模式识别理论与应用;沈肖波，男，1989年生，博士研究生，主要研究方向为模式识别、信息融合等;孙权森，男, 1963年生, 教授, 博士生导师，主要研究方向为模式识别理论与应用、图像分析与识别。主持国家自然科学基金、教育部博士点基金、江苏省自然科学基金、国防科工局民用航天预先研究项目、国家重大专项基础关键技术项目及其他省部级项目20余项。获得省部级奖励5项；获得国家发明专利3项，申请国家发明5项。发表学术论文100余篇，被SCI检索近30篇，主编著作教材4部.
通讯作者:孙权森,E-mail:sunquansen@njust.edu.cn.
更新日期/Last Update:
2017-08-25