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Face Recognition Based On Improved Support Vector Clustering

International Journal on Smart Sensing and Intelligent Systems
Volume 7 (2014): Issue 4 (January 2014)
By:
Open Access
|Dec 2014

References

  1. Jie Tian and Xin Yang, Biometric identification technology theory and applications, Electronic Industry Press, Beijing, 2005.
    Search in Google ScholarBack to article
  2. C. Cortes and V. Vapnik, “Support vector networks”, Machine Learning, Vol. 20, 1995, pp. 273-297.10.1007/BF00994018
    Search in Google ScholarBack to article
  3. V. Vapnik, “Three Fundamental Concepts of the Capacity of Learning Machines”, Physica A, Vol. 200, 1993, pp. 537-544.10.1016/0378-4371(93)90558-L
    Search in Google ScholarBack to article
  4. V. Vapnik, Statistical Learning Theory, J. Wiley, New York, 1998.
    Search in Google ScholarBack to article
  5. R. Collobert, S. Bengio and Y. Bengio, “A Parallel Mixture of SVMs for Very Large Scale Problems”, Neural Computation, Vol. 14, No. 5, 2002, pp. 1105-1114.10.1162/089976602753633402
    Search in Google ScholarBack to article
  6. J. X. Dong, A. Krzyzak and C. Y. Suen, “A fast parallel optimization for training support vector machine”, Proc. of the 3rd Int. Conf. Machine Learning Data Mining, Vol. LNAI 2734, 2003, pp. 96-105.10.1007/3-540-45065-3_9
    Search in Google ScholarBack to article
  7. G. Zanghirati and L. Zanni, “A parallel solver for large quadratic programs in training support vector machines”, Parallel Comput., Vol. 29, No. 4, 2003, pp. 535-551.10.1016/S0167-8191(03)00021-8
    Search in Google ScholarBack to article
  8. B. H. Guang, K. Z. Mao, C. K. Siew and D. S. Huang, “Fast modular network implementation for support vector machines”, IEEE Trans. Neural Netw., Vol. 16, No. 6, 2005, pp. 1651-1663.10.1109/TNN.2005.85795216342504
    Search in Google ScholarBack to article
  9. L. J. Cao, S. S. Keerthi and J. Q. Zhang, “Parallel Sequential Minimal Optimization for the Training of Support Vector Machines”, IEEE Trans. Neural Netw., Vol. 17, No. 4, 2006, pp. 1039-1049.10.1109/TNN.2006.87598916856665
    Search in Google ScholarBack to article
  10. S. Asharaf, M. N. Murty and S. K. Shevade, “Multiclass Core Vector Machine”, Proc. of ICML’07, 2007, pp. 41-48.10.1145/1273496.1273502
    Search in Google ScholarBack to article
  11. Francesco Orabona, “Better Algorithms for Selective Sampling”, in Proceedings of ICML’11, 2011, pp. 433-440.
    Search in Google ScholarBack to article
  12. Y. Liu, “Combining integrated sampling with SVM ensembles for learning from imbalanced datasets”, Information Processing and Management, Vol. 47, No. 4, 2011.10.1016/j.ipm.2010.11.007
    Search in Google ScholarBack to article
  13. M. Volpi, “Memory-Based Cluster Sampling for Remote Sensing Image Classification”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 8, 2012.10.1109/TGRS.2011.2179661
    Search in Google ScholarBack to article
  14. Abhimanyu Das and David Kempe, “Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection”, Proceedings of ICML’11, 2011, pp. 1057-1064.
    Search in Google ScholarBack to article
  15. I. W. Tsang, J. T. Kwok and P. M. Cheung, “Core vector machines: Fast SVM training on very large data sets”, Journal of Machine Learning Research, Vol. 6, 2005, pp. 363-392.
    Search in Google ScholarBack to article
  16. I. W. Tsang, A. Kocsor and J. T. Kwok, “Simpler Core vector machines with Enclosing Balls”, Proc. 24th Int. Conf. Machine Learning, 2007, pp. 911-918.10.1145/1273496.1273611
    Search in Google ScholarBack to article
  17. Ziquan H., “Algebraic feature extraction of image for recognition”, Pattern Recognition, Vol. 24, No. 3, 1991, pp. 211-219.10.1016/0031-3203(91)90063-B
    Search in Google ScholarBack to article
  18. Turk M. and Penfland A., “Face recognition using eigenfaces”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1991, pp. 586-591.
    Search in Google ScholarBack to article
  19. Bartlett M. S. and Movellan J. R., “Face Recognition by Independent Component Analysis”, IEEE Transactions on Neural Network, Vol. 13, No, 6, 2002, pp. 1450-1464.10.1109/TNN.2002.804287
    Search in Google ScholarBack to article
  20. Belhumeur P. N., Hespanha J. P., and Kriegman D. J., “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection”, IEEE Transactions no Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 711-720.10.1109/34.598228
    Search in Google ScholarBack to article
  21. Zhaoqi Bian and Xuegong Zhang, Pattern Recognition, Tsinghua Press, Beijing, 2004.
    Search in Google ScholarBack to article
  22. Yunhong Wang, Tieniu Tan, “Face Identification Based on Singular Value decomposition and Data Fusion”, CHINESE JOURNAL OF COMPUTERS, Vol. 23, No. 6, 2000, pp. 649-653.
    Search in Google ScholarBack to article
  23. Nefian A. V., Hayes M. H., “Hidden Markov Models for Face Recognition”, IEEE Intern. Conf. on Acoustics, Speech and Signal Processing, 1998, pp. 2721-2724.
    Search in Google ScholarBack to article
  24. Shuxian Zhu and Renjie Zhang, “Face recognition base on RBF neural network”, OPTICAL INSTRUMENTS, Vol. 30, No. 2, 2008, pp. 31-33.
    Search in Google ScholarBack to article
  25. Rui Xu and Wunsch, D., “Survey of Clustering Algorithms”, IEEE Transaction on Neural Networks, Vol. 16, No. 3, 2005, pp. 645-678.10.1109/TNN.2005.845141
    Search in Google ScholarBack to article
  26. Han J. and Kamber M., Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann, San Francisco, 2006.
    Search in Google ScholarBack to article
  27. Filippone M., Camastra F., and Masulli F., “A Survey of Kernel and Spectral Methods for Clustering”, Pattern Recognition, Vol. 41, No. 1, 2008, pp. 176-190.10.1016/j.patcog.2007.05.018
    Search in Google ScholarBack to article
  28. Burges C. J. C., “A Tutorial on Support Vector Machines for Pattern Recognition”, Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998, pp. 121-167.10.1023/A:1009715923555
    Search in Google ScholarBack to article
  29. Tax D. M. J., Duin R. P. W., “Support Vector Domain Description”, Pattern Recognition Letters, Vol. 20, No. 11-13, 1999, pp. 1191-1199.10.1016/S0167-8655(99)00087-2
    Search in Google ScholarBack to article
  30. Ben-Hur A., Horn D., Siegelmann H. T., Vapnik V., “Support Vector Clustering”, Journal of Machine Learning Research, Vol. 2, No. 12, 2001, pp. 125-137.
    Search in Google ScholarBack to article
  31. Scholkopf B., Williamson R., Smola A., Shawe-Taylor J., Platt J., “Support Vector Method for Novelty Detection”, Advances in Neural Information Processing System, Vol. 12, 2000, pp. 582-588.
    Search in Google ScholarBack to article
  32. Lee J. and Lee D., “An Improved Cluster Labeling Method for Support Vector Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 3, 2005, pp. 461464.10.1109/TPAMI.2005.4715747800
    Search in Google ScholarBack to article
  33. Camastra F. and Verri A., “A Novel Kernel Method for Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 5, 2005, pp. 801-805.10.1109/TPAMI.2005.8815875800
    Search in Google ScholarBack to article
  34. Tsang I. W., Kwok J. T., and Cheung P. M., “Core vector machines: Fast SVM training on very large data sets”, Journal of Machine Learning Research, Vol. 6, 2005, pp. 363-392.
    Search in Google ScholarBack to article
  35. S. Asharaf, M. N. Murty, and S. K. Shevade, “Multiclass Core Vector Machine”, in Proc. of ICML’07, 2007, pp. 41-48.10.1145/1273496.1273502
    Search in Google ScholarBack to article
  36. I. W. Tsang, J. T. Kwok, J. M. Zurada, “Generalized Core Vector Machines”, IEEE Trans. on Neural Networks, Vol. 17, No. 5, 2006, pp. 1126-1140.10.1109/TNN.2006.87812317001975
    Search in Google ScholarBack to article
  37. Y. Q. Wang, Y. Li, and L. Chang, “Approximate Minimum Enclosing Ball Algorithm with Smaller Core Sets for Binary Support Vector Machine”, in Proc. CCDC’2010, 2010, pp. 34043408.
    Search in Google ScholarBack to article
  38. Y. Q. Wang, X. T. Niu, and L. Chang, “Multiclass Core Vector Machine with Smaller Core Sets”, in Proc. CCDC’2010, 2010, pp. 525-530.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.21307/ijssis-2017-734 | Journal eISSN: 1178-5608
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Language: English
Page range: 1807 - 1829
Submitted on: Jul 6, 2014
Accepted on: Nov 5, 2014
Published on: Dec 1, 2014
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Face recognition,
biometric feature,
Support Vector Clustering,
kernel methods,
classification,
large-scale data
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2014 Yongqing Wang, Xiling Liu, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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