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Feature extraction using orthogonal discriminant local tangent space alignment

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Abstract

A novel algorithm called orthogonal discriminant local tangent space alignment (O-DLTSA) is proposed for supervised feature extraction. Derived from local tangent space alignment (LTSA), O-DLTSA not only inherits the advantages of LTSA which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. The experimental results of applying O-DLTSA to standard face databases demonstrate the effectiveness of the proposed method.

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Notes

  1. http://cvc.yale.edu/projects/yalefaces/yalefaces.html.

  2. http://www.cam-orl.co.uk/facedatabase.html.

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Acknowledgments

This work is supported by the grants of the National Science Foundation of China, Nos. 60705007, 60805021, 60975005, 60873012, 60905023 and 60872113, and the Knowledge Innovation Program of the Chinese Academy of Sciences. The authors would like to thank all the guest editors and anonymous reviewers for their constructive advices.

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Authors and Affiliations

  1. State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei, 230027, Anhui, China

    Ying-Ke Lei, Yang-Ming Xu, Jun-An Yang & Zhi-Guo Ding

  2. Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, 230031, Anhui, China

    Ying-Ke Lei & Jie Gui

  3. Department of Automation, University of Science and Technology of China, Hefei, 230027, Anhui, China

    Ying-Ke Lei

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  1. Ying-Ke Lei
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  2. Yang-Ming Xu
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  4. Zhi-Guo Ding
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  5. Jie Gui
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Correspondence to Jie Gui.

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Lei, YK., Xu, YM., Yang, JA. et al. Feature extraction using orthogonal discriminant local tangent space alignment. Pattern Anal Applic 15, 249–259 (2012). https://doi.org/10.1007/s10044-011-0231-0

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