Classification with the Hybrid of Manifold Learning and Gabor Wavelet

  • Junping Zhang
  • Chao Shen
  • Jufu Feng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


While manifold learning algorithms can discover intrinsic low-dimensional manifold embedded in the high-dimensional Euclidean space, the discriminant ability of the low-dimensional subspaces obtained by the algorithms is often lower than those obtained by the conventional dimensionality reduction approaches. Furthermore, the original feature vectors may include redundancy such as high-order correlation which cannot be removed by manifold learning algorithms. To address the two problems, we first employ Gabor wavelet to remove intrinsic redundancies of images and obtain a set of over-completed feature vectors. Then a supervised manifold learning algorithm (ULLELDA) is applied to project Gabor-based data and out-of-the-samples into a common low-dimensional subspace. Experiments in two FERET face databases indicate that Gabors indeed help supervised manifold learning to remarkably improve the discriminant ability of low-dimensional subspaces.


Face Recognition Linear Discriminant Analysis Discriminant Ability Face Database Gabor Wavelet 
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 2006

Authors and Affiliations

  • Junping Zhang
    • 1
    • 2
  • Chao Shen
    • 1
  • Jufu Feng
    • 3
  1. 1.Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science and EngineeringFudan UniversityShanghaiChina
  2. 2.The Key Laboratory of Complex Systems and Intelligence Science, Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.Center for Information Science, National Key Laboratory for Machine Perception, School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

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