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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

侯书东, 孙权森. 稀疏保持典型相关分析及在特征融合中的应用. 自动化学报, 2012, 38(4): 659-665. doi: 10.3724/SP.J.1004.2012.00659
引用本文: 侯书东, 孙权森. 稀疏保持典型相关分析及在特征融合中的应用. 自动化学报, 2012, 38(4): 659-665. doi: 10.3724/SP.J.1004.2012.00659
HOU Shu-Dong, SUN Quan-Sen. Sparsity Preserving Canonical Correlation Analysis with Application in Feature Fusion. ACTA AUTOMATICA SINICA, 2012, 38(4): 659-665. doi: 10.3724/SP.J.1004.2012.00659
Citation: HOU Shu-Dong, SUN Quan-Sen. Sparsity Preserving Canonical Correlation Analysis with Application in Feature Fusion. ACTA AUTOMATICA SINICA, 2012, 38(4): 659-665. 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
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

Sparsity Preserving Canonical Correlation Analysis with Application in Feature Fusion

  • 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.
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  • 收稿日期:  2011-04-29
  • 修回日期:  2011-07-01
  • 刊出日期:  2012-04-20

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