Face Recognition Based On Improved Support Vector Clustering

1Jie Tian and Xin Yang, Biometric identification technology theory and applications, Electronic Industry Press, Beijing, 2005. Search in Google Scholar

2C. Cortes and V. Vapnik, “Support vector networks”, Machine Learning, Vol. 20, 1995, pp. 273-297.10.1007/BF00994018 Search in Google Scholar

3V. 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 Scholar

4V. Vapnik, Statistical Learning Theory, J. Wiley, New York, 1998. Search in Google Scholar

5R. 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 Scholar

6J. 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 Scholar

7G. 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 Scholar

8B. 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 Scholar

9L. 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 Scholar

10S. 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 Scholar

11Francesco Orabona, “Better Algorithms for Selective Sampling”, in Proceedings of ICML’11, 2011, pp. 433-440. Search in Google Scholar

12Y. 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 Scholar

13M. 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 Scholar

14Abhimanyu 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 Scholar

15I. 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 Scholar

16I. 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 Scholar

17Ziquan 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 Scholar

18Turk M. and Penfland A., “Face recognition using eigenfaces”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1991, pp. 586-591. Search in Google Scholar

19Bartlett 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 Scholar

20Belhumeur 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 Scholar

21Zhaoqi Bian and Xuegong Zhang, Pattern Recognition, Tsinghua Press, Beijing, 2004. Search in Google Scholar

22Yunhong 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 Scholar

23Nefian 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 Scholar

24Shuxian Zhu and Renjie Zhang, “Face recognition base on RBF neural network”, OPTICAL INSTRUMENTS, Vol. 30, No. 2, 2008, pp. 31-33. Search in Google Scholar

25Rui 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 Scholar

26Han J. and Kamber M., Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann, San Francisco, 2006. Search in Google Scholar

27Filippone 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 Scholar

28Burges 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 Scholar

29Tax 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 Scholar

30Ben-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 Scholar

31Scholkopf 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 Scholar

32Lee 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 Scholar

33Camastra 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 Scholar

34Tsang 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 Scholar

35S. 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 Scholar

36I. 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 Scholar

37Y. 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 Scholar

38Y. 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 Scholar