Kernel Manifold Learning-Based Face Recognition

  • Jun-Bao Li
  • Shu-Chuan Chu
  • Jeng-Shyang Pan


Feature extraction with dimensionality reduction is an important step and essential process in embedding data analysis. Linear dimensionality reduction aims to develop a meaningful low-dimensional subspace in a high-dimensional input space such as PCA and LDA. LDA is to find the optimal projection matrix with Fisher criterion through considering the class labels, and PCA seeks to minimize the mean square error criterion.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Automatic Test and ControlHarbin Institute of TechnologyHarbinPeople’s Republic of China
  2. 2.School of Information and EngineeringFlinders University of South AustraliaBedford ParkAustralia
  3. 3.HIT Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhen CityPeople’s Republic of China

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