Extended semi-supervised fuzzy learning method for nonlinear outliers via pattern discovery

Applied Soft Computing Journal

Volumn 29, Issue , 2015, Pages 245-255

  Song, Xiaoning ^a,b,c   Liu, Zi ^d   Yang, Xibei ^c,d   Yang, Jingyu ^d   Qi, Yunsong ^c  

^a JIANGNAN UNIVERSITY   (China)

^b UNIVERSITY OF SURREY   (United Kingdom)

^c JIANGSU UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^d NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Fuzzy discriminant analysis; Image recognition; Outlier instances; Parameter estimation; Semi supervised clustering

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; DISCRIMINANT ANALYSIS; FACE RECOGNITION; HOPFIELD NEURAL NETWORKS; IMAGE RECOGNITION; LEARNING ALGORITHMS; STATISTICS; SUPERVISED LEARNING;

BETWEEN CLASS SCATTER; CLASSIFICATION RESULTS; HOPFIELD NEURAL NETWORKS (HNN); KERNEL FISHER DISCRIMINANT; LINEAR DISCRIMINANT ANALYSIS; OUTLIER INSTANCES; SEMI- SUPERVISED LEARNING; SEMI-SUPERVISED CLUSTERING;

PARAMETER ESTIMATION;

EID: 84921730338     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2014.12.034     Document Type: Article

References (59)

1. C. Gong, K. Fu, Q. Wu, E. Tu, and J. Yang Semi-supervised classification with pairwise constraints Neurocomputing 139 2014 130 137
2. N.N. Gu, D. Wang, M.Y. Fan, and D.Y. Meng A kernel-based sparsity preserving method for semi-supervised classification Neurocomputing 139 2014 345 356
3. B.K. Seoung, and R. Panaya Unsupervised feature selection using weighted principal components Exp. Syst. Appl. 38 2011 5704 5710
4. K.Z. Mao Identifying critical variables of principal components for unsupervised feature selection IEEE Trans. Syst. Man Cybern. B: Cybern. 35 2 2005 339 344
5. A. Daneshpazhouh, and A. Sami Entropy-based outlier detection using semi-supervised approach with few positive examples Pattern Recogn. Lett. 49 2014 77 84
6. J. Kersten Simultaneous feature selection and Gaussian mixture model estimation for supervised classification problems Pattern Recogn. 47 2014 2582 2595
7. Y. Liu, Q.C. Wang, Y. Jiang, and Y.Q. Lei Supervised locality discriminant manifold learning for head pose estimation Knowl. Based Syst. 66 2014 126 135
8. K. Than, T.B. Ho, and D.K. Nguyen An effective framework for supervised dimension reduction Neurocomputing 139 2014 397 407
9. M. Langkvist, L. Karlsson, and A. Loutfi A review of unsupervised feature learning and deep learning for time-series modeling Pattern Recogn. Lett. 42 2014 11 24
10. M.M.B. Ismail, and H. Frigui Unsupervised clustering and feature weighting based on Generalized Dirichlet mixture modeling Inf. Sci. 274 2014 35 54
11. V. Hautamaki, I. Karkkainen, and P. Franti Outlier detection using k-nearest neighbor graph Proceedings of the 17th International Conference on Pattern Recognition 2004 23 26
12. V.J. Hodge, and J. Austin A survey of outlier detection methodologies Artif. Intell. Rev. 22 2 2004 85 126
13. M.F. Jiang, S.S. Tseng, and C.M. Su Two-phase clustering process for outlier detection Pattern Recogn. Lett. 22 6-7 2001 691 700
14. V. Argyriou, S. Zafeiriou, and M. Petrou Optimal illumination directions for faces and rough surfaces for single and multiple light imaging using class-specific prior knowledge Comput. Vis. Image Underst. 125 2014 16 36
15. W.N. Wang, D. Cai, X.M. Xu, and A.W.C. Liew Visual saliency detection based on region descriptors and prior knowledge Signal Process. Image Commun. 29 2014 424 433
16. M. Jaime, A.G. Miguel, P. Domenec, and P. Maria Unsupervised texture-based image segmentation through pattern discovery Comput. Vis. Image Underst. 115 2011 1121 1133
17. X.Y. Jing, S. Li, D. Zhang, J. Yang, J.Y. Yang, and Supervised Unsupervised parallel subspace learning for large-scale image recognition IEEE Trans. Circuits Syst. Video Technol. 22 10 2012 1497 1511
18. Y. Xu, J.Y. Yang, J. Lu, and D.J. Yu An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments Pattern Recogn. 37 10 2004 2091 2094
19. D. You, and A.M. Martínez Bayes optimal kernel discriminant analysis CVPR 353 2010 3533 3538
20. A. Diaf, B. Boufama, and R. Benlamri Non-parametric Fisher's discriminant analysis with kernels for data classification Pattern Recogn. Lett. 34 5 2013 552 558
21. Y. Xu, J.Y. Yang, and J. Yang A reformative kernel Fisher discriminant analysis Pattern Recogn. 37 6 2004 1299 1302
22. P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman Eigenfaces vs. Fisherfaces: recognition using class specific linear projection IEEE Trans. Pattern Anal. Mach. Intell. 19 7 1997 711 720
23. H. Yu, and J. Yang A direct LDA algorithm for high-dimensional data - with application to face recognition Pattern Recogn. 34 10 2001 2067 2070
24. J. Yang, A.F. Frangi, J.Y. Yang, D. Zhang, and Z. Jin KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition IEEE Trans. Pattern Anal. Mach. Intell. 27 2 2005 230 244
25. X.X. Zhang, and Y.D. Jia A linear discriminant analysis framework based on random subspace for face recognition Pattern Recogn. 40 2007 2585 2591
26. K.C. Kwak, and W. Pedrycz Face recognition using a fuzzy Fisherface classifier Pattern Recogn. 38 10 2005 1717 1732
27. M. Wan, G. Yang, Z. Lai, and Z. Jin Feature extraction based on fuzzy local discriminant embedding with applications to face recognition IET Comput. Vis. 5 5 2011 301 308
28. Y. Cui, and L.Y. Fan Feature extraction using fuzzy maximum margin criterion Neurocomputing 86 2012 52 58
29. X.N. Song, Y.J. Zheng, X.J. Wu, X.B. Yang, and J.Y. Yang A complete fuzzy discriminant analysis approach for face recognition Appl. Soft Comput. 10 2010 208 214
30. M.N. Ahmed, S.M. Yamany, N. Mohamed, and A.A. Farag A modified fuzzy C-mean algorithm for bias field estimation and segmentation of MRI data IEEE Trans. Med. Imaging 21 3 2002 193 199
31. M. Omran, A. Engelbrecht, and A. Salman An overview of clustering methods Intell. Data Anal. 11 2007 583 605
32. D. Comaniciu, and P. Meer Mean shift: a robust approach toward feature space analysis IEEE Trans. Pattern Anal. 24 2002 603 619
33. J. Shi, and J. Malik Normalized cuts and image segmentation IEEE Trans. Pattern Anal. 22 2000 888 905
34. M. Ester, H.P. Kriegel, J. Sander, and X. Xu A density based algorithm for discovering clusters in large spatial databases with noise Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD) 1996 226 231
35. ORL database, The ORL face database at the AT&T (Olivetti) research laboratory. Available from: http://www.uk.research.att.com/facedatabase.html.
36. H.T. Zhao, P.C. Yuen, J.T. Kwok, and J.Y. Yang Incremental PCA based face recognition Proceedings of the 8th International Conference on Control, Automation, Robotics and Vision (ICARCV) Kunming, China, December 2004 687 691
37. P.J. Phillips, H. Moon, S.A. Rizvi, P.J. Rauss, and F.E.R.E.T. The evaluation methodology for face-recognition algorithms IEEE Trans. Pattern Anal. Mach. Intell. 22 10 2000 1090 1104
38. Yale database. Available from: http://cvc.yale.edu/projects/yalefaces/yalefaces.html.
39. L. Ljung System Identification: Theory For the User 2nd ed. 1999 Prentice Hall New Jersey, USA
40. U.P. Wen, K.M. Lan, and H.S. Shih A review of Hopfield neural networks for solving mathematical programming problems Eur. J. Oper. Res. 198 2009 675 687
41. Z.N. Hu, and S.N. Balakrishnan Parameter estimation in nonlinear systems using Hopfield neural networks J. Aircr. 42 1 2005 41 53
42. H. Alonso, T. Mendonça, and P. Rocha Hopfield neural networks for on-line parameter estimation Neural Netw. 22 2009 450 462
43. Z. Fan, Y. Xu, and D. Zhang Local linear discriminant analysis framework using sample neighbors IEEE Trans. Neural Netw. 22 7 2011 1119 1132
44. S.J. Kim, A. Magnani, and S.P. Boyd Robust fisher discriminant analysis Adv. Neural Inf. Process. Syst. 2005 659 666
45. Y. Xu Quaternion-based discriminant analysis method for color face recognition PLoS ONE 7 8 2012 e43493
46. Z. Lai, Y. Xu, J. Yang, J. Tang, and D. Zhang Sparse tensor discriminant analysis IEEE Trans. Image Process. 22 10 2013 3904 3915
47. Y. Xu, Q. Zhu, Z.Z. Fan, Y.W. Wang, and J.S. Pan From the idea of 'sparse representation' to a representation-based transformation method for feature extraction Neurocomputing 113 2013 168 176
48. J.M. Keller, M.R. Gray, and J.A. Givens A fuzzy k-nearest neighbor algorithm IEEE Trans. Syst. Man Cybern. 15 4 1985 580 585
49. M.P. Wachowiak, R. Smolíková, J.M. Zurada, and A.S. Elmaghraby Estimation of distribution parameters using neural networks IEEE Trans. Biomed. Eng. 49 6 2002 617 620
50. J.C. Dunn A fuzzy relative of the ISODATA process and its use in detecting compact well separated cluster J. Cybern. 3 3 1974 32 57
51. J.C. Bezdek Pattern Recognition with Fuzzy Objective Function Algorithms 1981 Kluwer Academic Publishers Norwell, MA, USA
52. J.C. Bezdek, L.O. Hall, and L.P. Clarke Review of MR image segmentation techniques using pattern recognition J. Cybern. 20 4 1993 1033 1048
53. Z.X. Ji, Q.S. Sun, and D.S. Xia A framework with modified fast FCM for brain MR images segmentation Pattern Recogn. 44 2011 999 1013
54. D.L. Pham, and J.L. Prince An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities Pattern Recogn. Lett. 20 1 1999 57 68
55. M. Breaban, and H. Luchian A unifying criterion for unsupervised clustering and feature selection Pattern Recogn. 44 2011 854 865
56. M. Kim, and R.S. Ramakrishna Some new indexes of cluster validity Pattern Recogn. Lett. 26 15 2005 2353 2363
57. J. Handl, and J. Knowles Improving the scalability of multiobjective clustering Proceedings of the Congress on Evolutionary Computation 2005 IEEE Press 2372 2379
58. B. Niu, Q. Yang, S.C.K. Shiu, and S.K. Pal Two-dimensional Laplacianfaces method for face recognition Pattern Recogn. 41 2008 3237 3243
59. J.G. Wang, Y.S. Lin, W.K. Yang, and J.Y. Yang Kernel maximum scatter difference based feature extraction and its application to face recognition Pattern Recogn. Lett. 29 2008 1832 1835