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Image Recognition with LPP Mixtures

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Computational Intelligence and Security (CIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3801))

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Abstract

Locality preserving projections (LPP) can find an embedding that preserves local information and discriminates data well. However, only one projection matrix over the whole data is not enough to discriminate complex data. In this paper, we proposed locality preserving projections mixture models (LPP mixtures), where the set of all data were partitioned into several clusters and a projection matrix for each cluster was obtained. In each cluster, We performed LPP via QR-decomposition, which is efficient computationally in under-sampled situations. Its theoretical foundation was presented. Experiments on a synthetic data set and the Yale face database showed the superiority of LPP mixtures.

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References

  1. Belhumeur, P.N., Hespanha, J.P., Kriengman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. PAMI 19, 711–720 (1997)

    Google Scholar 

  2. Dempster, P., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. 39, 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  3. He, X.F., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face Recognition Using Laplacianfaces. IEEE Trans. PAMI 27, 328–340 (2005)

    Google Scholar 

  4. Kim, H.C., Kim, D., Bang, S.Y.: Face recognition using LDA mixture model. Pattern Recognition Letters 24, 2815–2821 (2003)

    Article  Google Scholar 

  5. Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. PAMI 23, 228–233 (2001)

    Google Scholar 

  6. Tipping, M., Bishop, C.: Mixtures of probabilistic principal component analyzers. Neural Comput. 11, 443–482 (1999)

    Article  Google Scholar 

  7. Turk, M., Pentland, A.P.: Face Recognition Using Eigenfaces. In: IEEE Conf. Computer Vision and Pattern Recognition (1991)

    Google Scholar 

  8. Ye, J.P., Li, Q.: A Two-Stage Linear Discriminant Analysis via QR-Decomposition. IEEE Trans. PAMI 27, 929–941 (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Key Lab of Intelligent Computing & Signal Processing of Ministry of Education, Anhui University, Hefei, 230039, China

    SiBao Chen, Min Kong & Bin Luo

Authors
  1. SiBao Chen
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  2. Min Kong
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  3. Bin Luo
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Editor information

Editors and Affiliations

  1. Microelectronic Instiute, Xidian University, 710071, Xi’an, China

    Yue Hao

  2. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  3. School of Computer Science and Technology, Xidian University, Xi’an, China

    Yuping Wang

  4. Department of Computer Science, Hong Kong Baptist University, Hong Kong,  

    Yiu-ming Cheung

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  6. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  7. Key Laboratory of Computer Networks and Information Security (Ministry of Education), Xidian University, 710071, Xi’an, China

    Jianfeng Ma

  8. National Laboratory of Antennas and Microwave Technology, Xidian University, 710071, Xi’an, Shanxi, P.R. China

    Yong-Chang Jiao

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© 2005 Springer-Verlag Berlin Heidelberg

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Chen, S., Kong, M., Luo, B. (2005). Image Recognition with LPP Mixtures. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_149

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  • DOI: https://doi.org/10.1007/11596448_149

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30818-8

  • Online ISBN: 978-3-540-31599-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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