Information Technology Reference
In-Depth Information
53. R. Choudrey, S. Roberts, Variational mixture of bayesian independent component analysers.
Neural Comput. 15(1), 213-252 (2002)
54. M.E. Tipping, C.M. Bishop, Mixtures of probabilistic principal component analyzers. Neural
Comput. 11(2), 443-482 (1999)
55. Z. Ghahramani, M. Beal, Variational inference for Bayesian mixtures of factor analysers.
Adv. Neural Inf. Process. Syst. 12, 449-445 (2000)
56. C. Archambeau, N. Delannay, M. Verleysen, Mixtures of robust probabilistic principal
component analyzers. Neurocomputing 71(7-9), 1274-1282 (2008)
57. M. Svensén, C.M. Bishop, Robust Bayesian mixture modelling. Neurocomputing 64, 235-252
(2005)
58. 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)
59. S. Roberts, W.D. Penny, Mixtures of independent component analyzers. in Proceedings of
ICANN2001, Vienna, August 2001, pp. 527-534
60. J.A. Palmer, K. Kreutz-Delgado, S. Makeig, An Independent Component Analysis Mixture
Model with Adaptive Source Densities, Technical Report, UCSD, 2006
61. 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)
62. 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)
63. T. Yoshida, M. Sakagami, K. Yamazaki, T. Katura, M. Iwamoto, N. Tanaka, Extraction of
neural activity from in vivo optical recordings using multiple independent component
analysis. IEEJ Trans. Electron. Inf. Syst. 127(10), 1642-1650 (2007)
64. J.A. Palmer, S. Makeig, K. Kreutz-Delgado, B.D. Rao, Newton method for the ICA mixture
model. Proceedings of the 33rd IEEE International Conference on Acoustics, Speech, and
Signal, pp. 1805-1808, Las Vegas, USA, 2008
65. 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)
66. 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)
Search WWH ::




Custom Search