稀疏保持典型相关分析及在特征融合中的应用

侯书东; 孙权森

doi:10.3724/SP.J.1004.2012.00659

稀疏保持典型相关分析及在特征融合中的应用

doi: 10.3724/SP.J.1004.2012.00659

侯书东,
孙权森^,

1.
南京理工大学计算机科学与技术学院南京 210094

详细信息

通讯作者:
孙权森南京理工大学教授. 主要研究方向为模式识别, 图像处理与遥感信息处理. E-mail: qssun@126.com

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出版历程
- 收稿日期: 2011-04-29
- 修回日期: 2011-07-01
- 刊出日期: 2012-04-20

Sparsity Preserving Canonical Correlation Analysis with Application in Feature Fusion

HOU Shu-Dong,
SUN Quan-Sen^,

1.
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094

摘要

摘要: 稀疏保持投影(Sparsity preserving projections, SPP)由于保持了数据间的稀疏重构性, 因而获取的投影向量满足旋转、尺度和平移的不变性, 并能够在无标签的情况下提取样本的自然鉴别信息, 在人脸识别领域取得了较为成功的应用. 本文在典型相关分析(Canonical correlation analysis, CCA)的基础上引入稀疏保持项, 提出一种稀疏保持典型相关分析(Sparsity preserving canonical correlation analysis, SPCCA). 该方法不仅实现了两组特征集鉴别信息的有效融合, 同时对提取特征间的稀疏重构性加以约束, 增强了特征的表示和鉴别能力. 在多特征手写体字符集与人脸数据集上的实验结果表明, SPCCA比CCA具有更优的识别性能.
- 典型相关分析 (CCA) /
- 稀疏保持投影(SPP) /
- 稀疏保持典型相关分析(SPCCA) /
- 特征融合
Abstract: Sparsity preserving projections (SPP) aim to preserve the sparse reconstructive relationship among the data and have been successfully applied in face recognition. The projections are invariant to rotations, rescalings, and translations of the data, and more importantly, they contain natural discriminating information even without class labels. Enlightened by this, we propose a sparsity preserving canonical correlation analysis (SPCCA). It can not only fuse the discriminative information of two feature sets efficiently, but also constrains the sparse reconstructive relationship among each feature set in order to increase the representational power and has good discrimination capability of the feature extracted by SPCCA. Experimental results on the multiple feature databases and face databases show that the proposed SPCCA is better than CCA.
- Canonical correlation analysis (CCA) /
- sparsity preserving projections (SPP) /
- sparsity preserving CCA (SPCCA) /
- feature fusion

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