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  • Issue No. 8 - August
  • Abstract - Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics"
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Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics"
August 2008 (vol. 30 no. 8)
pp. 1503-1504
In [1], UDP is proposed to address the limiatation of LPP for the clustering and classification tasks. In this communication, we show that the basic ideas of UDP and LPP are identical. In particular, UDP is just a simplified version of LPP on the assumption that the local density is uniform.

[1] J. Yang, D. Zhang, J.-Y. Yang, and B. Niu, “Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 650-664, Apr. 2007.
[2] X. He and P. Niyogi, “Locality Preserving Projections,” Proc. Conf. Advances in Neural Information Processing System, 2003.
[3] X. He, S. Yan, Y. Hu, P. Niyogi, and H.-J. Zhang, “Face Recognition Using Laplacianfaces,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, Mar. 2005.
[4] X. He, D. Cai, and W. Min, “Statistical and Computational Analysis of Locality Preserving Projection,” Proc. 22nd Int'l Conf. Machine Learning, pp.281-288, 2005.
[5] D. Cai, X. He, and J. Han, “Document Clustering Using Locality Preserving Indexing,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 12, pp. 1624-1637, Dec. 2005.

Index Terms:
Statistical, Feature evaluation and selection, Face and gesture recognition
Citation:
Weihong Deng, Jiani Hu, Jun Guo, Honggang Zhang, Chuang Zhang, "Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics"," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1503-1504, Aug. 2008, doi:10.1109/TPAMI.2007.70783
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