Fast PCA and LDA for JPEG Images

  • Weilong Chen
  • Meng Joo Er
  • Shiqian Wu
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 30)


In this paper, we prove that the Principal Component Analysis (PCA) and the Linear Discriminant Analysis (LDA) can be directly implemented in the DCT (Discrete Cosine Transform) domain and the results are exactly the same as the one obtained from the spatial domain. In some applications, compressed images are desirable to reduce the storage requirement. For images compressed using the DCT, e.g., in JPEG or MPEG standard, the PCA and LDA can be directly implemented in the DCT domain such that the inverse DCT transform can be skipped and the dimensionality of the original data can be initially reduced to cut down computational cost.


Principal Component Analysis Face Recognition Linear Discriminant Analysis Discrete Cosine Transform Projection Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Weilong Chen
    • 1
  • Meng Joo Er
    • 1
  • Shiqian Wu
    • 2
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore
  2. 2.Institute for Infocomm ResearchSingapore

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