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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling

Part of the book series: Springer Theses ((Springer Theses,volume 4))

Abstract

From our most early experiences with reality, we start to recognize patterns in the surrounding environment. This allows us as human beings to be aware of the different objects that we are related to. The scope of pattern recognition is broad since it is observed at different levels in the world. This awareness occurs for a cell that divides and specializes itself and for an expert standing in front of a painting trying to make a distinction between the pure object and the pure subject of that object.

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References

  1. C.J.D.M. Verhagen, Some general remarks about pattern recognition: its definition; its relation with other disciplines; a literature survey. Pattern Recognit. 7(3), 109–116 (1975)

    Article  MATH  Google Scholar 

  2. J.C. Bezdek, S.K. Pal, Fuzzy Models for Pattern Recognition: Methods That Search for Structures in Data (IEEE press, New York, 1992)

    Google Scholar 

  3. H.A. Simon, Science Seeks Parsimony, Not Simplicity: Searching for Pattern In Phenomena. in Simplicity, Inference and Modelling (Keeping it Sophisticatedly Simple), ed. by A. Zellner, H.A. Keuzenkamp, M. McAleer (Cambridge University Press, Cambridge, 2004)

    Google Scholar 

  4. V. Castelli, T.M. Cover, The relative value of labelled and unlabelled samples in pattern recognition with an unknown mixing parameter. IEEE Trans. Inf. Theory 42(6), 2102–2117 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  5. I. Cohen, F.G. Cozman, N. Sebe, M.C. Cirelo, T.S. Huang, Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction. IEEE Trans. Pattern Anal. Mach. Learn. 26(12), 1553–1567 (2004)

    Article  Google Scholar 

  6. O. Chapelle, B. Schölkopf, A Semi-supervised Learning (MIT Press, Zien, 2006)

    Book  Google Scholar 

  7. S. Haykin, Intelligent Signal Processing (Wiley-IEEE Press, New York, 2001)

    Book  Google Scholar 

  8. A.K. Jain, Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)

    Article  Google Scholar 

  9. A.R. Webb, Statistical Pattern Recognition (Wiley, New York, 2002)

    Book  MATH  Google Scholar 

  10. T.W. Lee, M.S. Lewicki, T.J. Sejnowski, ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1078–1089 (2000)

    Article  Google Scholar 

  11. R. Choudrey, S. Roberts, Variational mixture of bayesian independent component analysers. Neural Comput. 15(1), 213–252 (2002)

    Article  Google Scholar 

  12. P. Comon, Independent component analysis—a new concept? Signal Process. 36(3), 287–314 (1994)

    Article  MATH  Google Scholar 

  13. A. Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis (Wiley, New York, 2001)

    Book  Google Scholar 

  14. A. Cichocki, S. Amari, Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications (Wiley, New York, 2001)

    Google Scholar 

  15. T.W. Lee, Independent Component Analysis—Theory and Applications (Kluwer Academic Publishers, Boston, 1998)

    MATH  Google Scholar 

  16. S. Roberts, R. Everson, Independent Component Analysis—Principles and Practice (Cambridge University Press, Cambridge, 2001)

    MATH  Google Scholar 

  17. A. Cichocki, R. Zdunek, A.H. Phan, S. Amari, Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation (Wiley, New York, 2009)

    Google Scholar 

  18. P. Comon, C. Jutten (eds.), Handbook of Blind Source Separation Independent Component Analysis and Applications (Academic Press, Oxford, 2010)

    Google Scholar 

  19. V. Zarzoso, A. Nandi, Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation. IEEE Trans. Biomed. Eng. 48(1), 12–18 (2001)

    Article  Google Scholar 

  20. J.J. Rieta, F. Castells, C. Sanchez, V. Zarzoso, Atrial activity extraction for atrial analysis using blind source separation. IEEE Trans. Biomed. Eng. 51(7), 1176–1186 (2004)

    Article  Google Scholar 

  21. R. Llinares, J. Igual, A. Salazar, A. Camacho, Semi-blind source extraction of atrial activity by combining statistical and spectral features. Digit. Signal Process. 21(2), 391–403 (2011)

    Article  Google Scholar 

  22. S. Makeig, J. Onton, in A Trial-by-Trial Pattern Approach to Event-Related EEG Analysis: ERP Features and EEG Dynamics: An ICA Perspective, ed. by S. Luck, E. Kappenman. Oxford Handbook of Event-Related Potential Components (Oxford University Press, Oxford, 2009)

    Google Scholar 

  23. D.M. Groppe, S. Makeig, M. Kutas, Identifying reliable independent components via split-half comparisons. Neuroimage 45, 1199–1211 (2009)

    Article  Google Scholar 

  24. Y. Karklin, M.S. Lewicki, Learning higher-order structures in natural images. Netw. Comput. Neural Syst. 14, 483–499 (2003)

    Article  Google Scholar 

  25. T.W. Lee, M.S. Lewicki, Unsupervised image classification, segmentation, and enhancement using ICA mixture models. IEEE Trans. Image Process. 11(3), 270–279 (2002)

    Article  Google Scholar 

  26. N.H. Mollah, M. Minami, S. Eguchi, Exploring latent structure of mixture ICA models by the Minimum ß-Divergence method. Neural Comput. 18, 166–190 (2005)

    Article  Google Scholar 

  27. C.A. Shah, M.K. Arora, P.K. Varshney, Unsupervised classification of hyperspectral data: an ICA mixture model based approach. Int. J. Remote Sens. 25(2), 481–487 (2004)

    Article  Google Scholar 

  28. C.A. Shah, P.K. Varshney, M.K. Arora, ICA mixture model algorithm for unsupervised classification of remote sensing imagery. Int. J. Remote Sens. 28(8), 1711–1731 (2007)

    Article  Google Scholar 

  29. U.M. Bae, T.W. Lee, S.Y. Lee, Blind signal separation in teleconferencing using the ICA mixture model. Electron. Lett. 37(7), 680–682 (2000)

    Article  MathSciNet  Google Scholar 

  30. K. Chan, T.W. Lee, T.J. Sejnowski, Variational learning of clusters of undercomplete nonsymmetric independent components. J. Mach. Learn. Res. 3, 99–114 (2002)

    MathSciNet  Google Scholar 

  31. M.H. Goldbaum, A.P.A. Sample, Z. Zhang, K. Chan, J. Hao, T.W. Lee, C. Boden, C. Bowd, R. Bourne, L. Zangwill, T. Sejnowski, D. Spinak, R.N. Weinreb, Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects. Investig. Ophthalmol. Vis. Sci. 46(10), 3676–3683 (2005)

    Article  Google Scholar 

  32. T.P. Jung, S. Makeig, T.W. Lee, M. J. McKeown, G. Brown, A. J. Bell, T. J. Sejnowski, Independent component analysis of biomedical signals. Proceedings of the 2nd International Workshop on Independent Component Analysis and Signal Separation, pp. 633–644, 2000

    Google Scholar 

  33. C.T. Lin, W.C. Cheng, S.F. Liang, An on-line ICA-mixture-model-based self-constructing fuzzy neural network. IEEE Trans. Circuits Syst. 52(1), 207–221 (2005)

    Article  MathSciNet  Google Scholar 

  34. X. Hu, A. Shimizu, H. Kobatake, S. Nawano, Aplying ICA mixture analysis for segmenting liver from multi-phase abdominal CT images. Lect. Notes Comput. Sci. 3150, 54–61 (2004)

    Article  Google Scholar 

  35. I. Guyon, A. Elisseeff, An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  36. L. Devroye, L. Györfi, G. Lugosi, A Probabilistic Theory of Pattern Recognition (Springer, New York, 1996)

    Book  MATH  Google Scholar 

  37. B.W. Silverman, Density Estimation for Statistics and Data Analysis (Chapman and Hall, London, 1985)

    Google Scholar 

  38. D.J. Mackay, Information Theory, Inference, and Learning Algorithms (Cambridge University Press, Cambridge, 2004)

    Google Scholar 

  39. A.K. Jain, M.N. Murty, P.J. Flynn, Data clustering: a review. ACM Comput. Surv. 31(3) (1999)

    Google Scholar 

  40. M. Haldiki, Y. Batistakis, M. Vazirgiannis, On clustering validation techniques. J. Intell. Inf. Syst. 17(2–3), 107–145 (2001)

    Google Scholar 

  41. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, New York, 1981)

    Book  MATH  Google Scholar 

  42. J. Zeng, C. Wu, W. Wang, Multi-grain hierarchical topic extraction algorithm for text mining. Expert Syst. Appl. 37, 3202–3208 (2010)

    Article  Google Scholar 

  43. R. Navigli, Word sense disambiguation: a survey. ACM Comput. Surv. 41(2), 10:1–10:69 (2009)

    Google Scholar 

  44. H. Azzag, G. Venturini, A. Oliver, C. Guinot, A hierarchical ant based clustering algorithm and its use in three real-world applications. Eur. J. Oper. Res. 179, 906–922 (2007)

    Article  MATH  Google Scholar 

  45. J.L. Seng, J.T. Lai, An Intelligent information segmentation approach to extract financial data for business valuation. Expert Syst. Appl. 37, 6515–6530 (2010)

    Article  Google Scholar 

  46. J.A. Vilar, A.M. Alonso, J.M. Vilar, Non-linear time series clustering based on non-parametric forecast densities. Comput. Stat. Data Anal. 54, 2850–2865 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  47. B. Andreopoulos, A. An, X. Wang, M. Schroeder, A roadmap of clustering algorithms: finding a match for a biomedical application. Briefings Bioinf. 10(3), 297–314 (2009)

    Article  Google Scholar 

  48. D.T. Pham, A.A. Afify, Clustering techniques and their applications in engineering. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 221(11), 1445–1459 (2007)

    Article  Google Scholar 

  49. S. Roweis, Z. Ghahramani, in Learning Nonlinear Dynamical Systems Using the EM Algorithm, ed. by S. Haykin. Kalman Filtering and Neural Networks (Wiley, New York, 2001), pp. 175–220

    Google Scholar 

  50. J.V. Candy, Bootstrap particle filtering. IEEE Signal Process. Mag. 24(4), 73–85 (2007)

    Article  Google Scholar 

  51. R. Chen, J.S. Liu, Mixture Kalman filters. J. Royal Stat. Soc. Ser. B 62, 493–508 (2000)

    Article  MATH  Google Scholar 

  52. J. Zhou, X.P. Zhan, Hidden Markov Model Framework Using Independent Component Analysis Mixture Model. Proceedings of the 31st IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. V:553–556, Toulouse, 2006

    Google Scholar 

  53. L.R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)

    Article  Google Scholar 

  54. D.H. Milone, L.E. Di Persia, M.E. Torres, Denoising and recognition using hidden Markov models with observation distributions modeled by hidden Markov trees. Pattern Recognit. 43, 1577–1589 (2010)

    Article  MATH  Google Scholar 

  55. X. Ma, D. Schonfeld, A.A. Khokhar, Video event classification and image segmentation based on noncausal multidimensional hidden Markov Models. IEEE Trans. Image Process. 18(6), 1304–1313 (2009)

    Google Scholar 

  56. F. Matta, J.L. Dugelay, Person recognition using facial video information: a state of the art. J. Vis. Lang. Comput. 20, 180–187 (2009)

    Article  Google Scholar 

  57. D. Kulic, W. Takano, Y. Nakamura, Online segmentation and clustering from continuous observation of whole body motions. IEEE Trans. Robot. 25(5), 1158–1166 (2009)

    Google Scholar 

  58. Z. He, X. You, Y.Y. Tang, Writer identification of Chinese handwriting documents using hidden Markov tree model. Pattern Recognit. 41(4), 1295–1307 (2008)

    Article  MATH  Google Scholar 

  59. C. Neuper, W. Klimesch, Event-Related Dynamics of Brain Oscillations: Progress in Brain Research Series, vol 159 (Elsevier, Amsterdam, 2006)

    Google Scholar 

  60. J. Kohlmorgen, K.R. Muëller, J. Rittweger, K. Pawelzik, Identification of nonstationary dynamics in physiological recordings. Biol. Cybern. 83(1), 73–84 (2000)

    Article  MATH  Google Scholar 

  61. M. Sansalone, W.B. Streett, Impact-Echo: Non-Destructive Evaluation of Concrete and Masonry (Bullbrier Press, Ithaca, 1997)

    Google Scholar 

  62. R.E. Taylor, M.J. Aitken, Chronometric Dating in Archaeology: Advances in Archaeological and Museum Science Series, vol. 2 (Springer, New York, 1997)

    Google Scholar 

  63. A. Salazar, L. Vergara, A. Serrano, J. Igual, A general procedure for learning mixtures of independent component analyzers. Pattern Recognit. 43(1), 69–85 (2010)

    Article  MATH  Google Scholar 

  64. J.F. Cardoso, High-order contrasts for independent component analysis. Neural Comput. 11(1), 157–192 (1999)

    Article  MathSciNet  Google Scholar 

  65. A.J. Bell, T.J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159 (1995)

    Article  Google Scholar 

  66. R. Boscolo, H. Pan, Independent component analysis based on nonparametric density estimation. IEEE Trans. Neural Netw. 15(1), 55–65 (2004)

    Article  Google Scholar 

  67. E.G. Learned-Miller, J.W. Fisher, ICA using spacings estimates of entropy. J. Machine Learn. Res. 4, 1271–1295 (2003)

    MathSciNet  Google Scholar 

  68. A. Hyvärinen, P.O. Hoyer, M. Inki, Topographic independent component analysis. Neural Comput. 13(7), 1527–1558 (2001)

    Article  MATH  Google Scholar 

  69. A. Hyvärinen, J. Hurri, Blind separation of sources that have spatiotemporal variance dependencies. Signal Process. Special Issue Indep. Compon. Anal. Beyond 84(2), 247–254 (2004)

    MATH  Google Scholar 

  70. H.J. Park, T.W. Lee, Capturing nonlinear dependencies in natural images using ICA and mixture of Laplacian distribution. Neurocomputing 69, 1513–1528 (2006)

    Article  Google Scholar 

  71. F.R. Bach, M.I. Jordan, Beyond independent components: trees and clusters. J. Mach. Learn. Res. 3, 1205–1233 (2003)

    MathSciNet  Google Scholar 

  72. O. Chapelle, B. Schölkopf, A. Zien, Semi-supervised Learning (MIT Press, Cambridge, 2006)

    Book  Google Scholar 

  73. K.H. Jeong, J.W. Xu, D. Erdogmus, J.C. Principe, A new classifier based on information theoretic learning with unlabelled data. Neural Netw. 18, 719–726 (2005)

    Article  Google Scholar 

  74. D. Erdogmus, J.C. Principe, From linear adaptive filtering to nonlinear information processing—the design and analysis of information processing systems. IEEE Signal Process. Mag. 23(6), 14–33 (2006)

    Article  MathSciNet  Google Scholar 

  75. C.W. Hesse, C.J. James, On semi-blind source separation using spatial constraints with applications in EEG Analysis. IEEE Trans. Biomed. Eng. 53(12–1), 2525–2534 (2006)

    Article  Google Scholar 

  76. J. Even, K. Sugimoto, An ICA approach to semi-blind identification of strictly proper systems based on interactor polynomial matrix. Int. J. Robust Nonlinear Control 17, 752–768 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  77. Z. Ding, T. Ratnarajah, C.F.N. Cowan, HOS-based semi-blind spatial equalization for MIMO rayleigh fading channels. IEEE Trans. Signal Process. 56(1), 248–255 (2008)

    Article  MathSciNet  Google Scholar 

  78. L. Vergara, J. Gosálbez, J.V. Fuente, R. Miralles, I. Bosch, A. Salazar, A. Lopez, L. Domínguez, Ultrasonic nondestructive testing on marble block rocks. Mater. Evaluation 62(1), 73–78 (2004)

    Google Scholar 

  79. T.W. Lee, M. Girolami, T.J. Sejnowski, Independent component analysis using an extended InfoMax algorithm for mixed sub-gaussian and super-gaussian sources. Neural Comput. 11(2), 417–441 (1999)

    Article  Google Scholar 

  80. A.J. Bell, T.J. Sejnowski, The “Independent Components” of natural scenes are edge filters. Vis. Res. 37(23), 3327–3338 (1997)

    Article  Google Scholar 

  81. A. Hyvärinen, E. Oja, A fast fixed-point algorithm for independent component analysis. Neural Comput. 9(7), 1483–1492 (1998)

    Article  Google Scholar 

  82. A. Hyvarinen, Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999)

    Article  Google Scholar 

  83. J.H. Van Hateren, A. van der Shaaf, Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. R. Soc. Lond. B 265, 359–366 (1998)

    Article  Google Scholar 

  84. Y. Matsuda, K. Yamaguchi, Linear multilayer ICA generating hierarchical edge detectors. Neural Comput. 19(1), 218–230 (2007)

    Article  MATH  Google Scholar 

  85. T.S. Lee, D. Mumford, Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. A 20(7), 1434–1448 (2003)

    Article  Google Scholar 

  86. A. Salazar, L. Vergara, J. Igual, J. Gosalbez, Blind source separation for classification and detection of flaws in impact-echo testing. Mech. Syst. Signal Process. 19(6), 1312–1325 (2005)

    Article  Google Scholar 

  87. Y. Huang, J. Benesty, J. Chen, Acoustic MIMO Signal Processing (Springer, Berlin, 2006)

    MATH  Google Scholar 

  88. A. Salazar, L. Vergara, R. Llinares, Learning material defect patterns by separating mixtures of independent component analyzers from NDT Sonic Signals. Mech. Syst. Signal Process. 24(6), 1870–1886 (2010)

    Article  Google Scholar 

  89. M. Jobert, H. Shulz, P. Jähnig, C. Tismer, F. Bes, H. Escola, A computerized method for detecting episodes of wakefulness during sleep based on the Alpha slow-wave index (ASI). Sleep 17(1), 37–46 (1994)

    Google Scholar 

  90. R. Felder, L. Silverman, Learning and teaching styles. J. Eng. Educ. 78(7), 674–681 (1988)

    Google Scholar 

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  1. Department of Communications, School of Telecommunication Engineering, Polytechnic University of Valencia, Camino de Vera s/n, 46022, Valencia, Spain

    Addisson Salazar

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Salazar, A. (2013). Introduction. In: On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling. Springer Theses, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30752-2_1

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  • DOI: https://doi.org/10.1007/978-3-642-30752-2_1

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