Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models

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Wang, Quan;
  • Subject: Computer Science - Computer Vision and Pattern Recognition
    arxiv: Computer Science::Computer Vision and Pattern Recognition
    acm: ComputingMethodologies_PATTERNRECOGNITION | ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION

Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which better exploits the complicated spatial structure of high-dimensional features. In this paper, we first review... View more
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