Introduction - On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling - page 27

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)
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)
59. C. Neuper, W. Klimesch, Event-Related Dynamics of Brain Oscillations: Progress in Brain
Research Series, vol 159 (Elsevier, Amsterdam, 2006)
60. J. Kohlmorgen, K.R. Muëller, J. Rittweger, K. Pawelzik, Identification of nonstationary
dynamics in physiological recordings. Biol. Cybern. 83(1), 73-84 (2000)
61. M. Sansalone, W.B. Streett, Impact-Echo: Non-Destructive Evaluation of Concrete and
Masonry (Bullbrier Press, Ithaca, 1997)
62. R.E. Taylor, M.J. Aitken, Chronometric Dating in Archaeology: Advances in Archaeological
and Museum Science Series, vol. 2 (Springer, New York, 1997)
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)
64. J.F. Cardoso, High-order contrasts for independent component analysis. Neural Comput.
11(1), 157-192 (1999)
65. A.J. Bell, T.J. Sejnowski, An information-maximization approach to blind separation and
blind deconvolution. Neural Comput. 7, 1129-1159 (1995)
66. R. Boscolo, H. Pan, Independent component analysis based on nonparametric density
estimation. IEEE Trans. Neural Netw. 15(1), 55-65 (2004)
67. E.G. Learned-Miller, J.W. Fisher, ICA using spacings estimates of entropy. J. Machine
Learn. Res. 4, 1271-1295 (2003)
68. A. Hyvärinen, P.O. Hoyer, M. Inki, Topographic independent component analysis. Neural
Comput. 13(7), 1527-1558 (2001)
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)
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)
71. F.R. Bach, M.I. Jordan, Beyond independent components: trees and clusters. J. Mach. Learn.
Res. 3, 1205-1233 (2003)
72. O. Chapelle, B. Schölkopf, A. Zien, Semi-supervised Learning (MIT Press, Cambridge, 2006)
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)
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)
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)
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)
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)
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)
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)
80. A.J. Bell, T.J. Sejnowski, The ''Independent Components'' of natural scenes are edge filters.
Vis. Res. 37(23), 3327-3338 (1997)
81. A. Hyvärinen, E. Oja, A fast fixed-point algorithm for independent component analysis.
Neural Comput. 9(7), 1483-1492 (1998)