Independent Component Analysis and Blind Signal Separation: 6th International Conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006, ProceedingsJustinian Rosca, Deniz Erdogmus, Jose C. Principe, Simon Haykin This book constitutes the refereed proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2006, held in Charleston, SC, USA, in March 2006. The 120 revised papers presented were carefully reviewed and selected from 183 submissions. The papers are organized in topical sections on algorithms and architectures, applications, medical applications, speech and signal processing, theory, and visual and sensory processing. |
Contents
Algorithms and Architectures | 1 |
Separation of Nonlinear Image Mixtures by Denoising Source Separation | 8 |
SecondOrder Separation of Multidimensional Sources with Constrained | 16 |
Speech and Signal Processing | 23 |
Fast Kernel Density Independent Component Analysis | 24 |
Family | 32 |
SecondOrder Blind Identification of Underdetermined Mixtures | 40 |
Differential Fast FixedPoint BSS for Underdetermined Linear | 48 |
Topographic Independent Component Analysis of Gene Expression | 462 |
Derivation of Atrial Surface Reentries Applying ICA to the Standard | 478 |
Performance Study of Convolutive BSS Algorithms Applied to | 495 |
Comparison of ICA Algorithms for the Isolation of Biological Artifacts | 511 |
A Novel Normalization and Regularization Scheme for Broadband | 527 |
Convolutive Demixing with Sparse Discrete Prior Models for Markov | 544 |
Harmonic Source Separation Using Prestored Spectra | 561 |
Utilization of Blind Source Separation Algorithms for MIMO Linear | 577 |
Equivariant Algorithms for Estimating the StrongUncorrelating | 57 |
Blind Source Separation of Postnonlinear Mixtures Using Evolutionary | 66 |
Model Structure Selection in Convolutive Mixtures | 74 |
Estimating the Information Potential with the Fast Gauss Transform | 82 |
An EM Method for Spatiotemporal Blind Source Separation Using | 98 |
New Permutation Algorithms for Causal Discovery Using | 115 |
Sparse Coding for Convolutive Blind Audio Source Separation | 132 |
A Novel Dimension Reduction Procedure for Searching NonGaussian | 151 |
An Extension of ICA to Multivariate | 165 |
Blind Separation of Underwater Acoustic Signals | 181 |
Recursive Generalized Eigendecomposition for Independent Component | 198 |
ICA Based Semisupervised Learning Algorithm for BCI Systems | 214 |
Quadratic MIMO Contrast Functions for Blind Source Separation in | 230 |
Efficient Separation of Convolutive Image Mixtures | 246 |
Quasirange | 262 |
Separation of Periodically TimeVarying Mixtures Using SecondOrder | 278 |
Riemannian Optimization Method on the Flag Manifold | 295 |
Relating HigherOrder Statistics | 311 |
Tracking and Implementation | 327 |
FixedPoint Complex ICA Algorithms for the Blind Separation | 343 |
Undoing the Affine Transformation Using Blind Source Separation | 360 |
Source Separation of Astrophysical Ice Mixtures | 368 |
Global Noise Elimination from ELF Band Electromagnetic Signals | 384 |
On the Performance of a HOSBased ICA Algorithm in BSS of Acoustic | 400 |
Analysis | 406 |
Blind Spatial Multiplexing Using Order Statistics for TimeVarying | 414 |
Medical Applications | 430 |
Cogito Componentiter Ergo | 446 |
Blind Separation of Sparse Sources Using Jeffreys Inverse Prior | 593 |
ICABased Speech Features in the Frequency Domain | 609 |
Nonnegativity Sparseness | 617 |
Comparison of Two Approaches | 633 |
ICA and BinaryMaskBased Blind Source Separation with Small | 649 |
Estimating the Spatial Position of Spectral Components in Audio | 666 |
Two TimeFrequency RatioBased Blind Source Separation Methods | 682 |
Nonnegative Matrix Factor 2D Deconvolution for Blind Single Channel | 700 |
Speech Enhancement in ShortWave Channel Based on ICA | 708 |
SingleChannel Mixture Decomposition Using Bayesian Harmonic | 722 |
Speech Enhancement Using ICA with EMDBased Reference | 739 |
On the Identifiability Testing in Blind Source Separation Using | 755 |
Postnonlinear Underdetermined ICA by Bayesian Statistics | 773 |
Average Convergence Behavior of the FastICA Algorithm for Blind | 788 |
Multivariate Scale Mixture of Gaussians Modeling | 799 |
Global Analysis of Log Likelihood Criterion | 815 |
Analysis of Source Sparsity and Recoverability for SCA Based Blind | 831 |
Kernel Principal Components Are Maximum Entropy Projections | 846 |
Instantaneous MISO Separation of BPSK Sources | 862 |
Contrast Functions for Blind Source Separation Based | 876 |
Local Convergence Analysis of FastICA | 893 |
Testing Significance of Mixing and Demixing Coefficients in | 901 |
Uniqueness of NonGaussian Subspace Analysis | 917 |
Visual and Sensory Processing | 934 |
An Easily Computable Eight Times Overcomplete ICA Method | 950 |
Nonnegative Matrix Factorization Approach to Blind Image | 966 |
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Common terms and phrases
affine transformation amplitude applied approach assumed atrial Berlin Heidelberg 2006 blind separation blind signal blind signal separation blind source separation channels clustering coefficients convergence convolutive mixtures correlation corresponding covariance criterion data set defined denoising denotes density desired source signal diagonal distribution eigenvector entropy equation error estimated experiments extraction FastICA filter flag manifold frequency function Gaussian genes global noise gradient ICA algorithms IEEE IEEE Trans images Independent Component Analysis iterations kurtosis learning algorithm linear LNCS manifold minimization mixing matrix mutual information Neural Networks non-Gaussian non-negative matrix factorization nonlinear observed signals obtained optimal order statistics orthogonal output P-ICA paper parameters performance permutation preprocessing problem Proc Rosca samples selection sensor Signal Processing signal separation simulations solution sparse spectral Springer-Verlag Berlin Heidelberg statistically independent Stiefel manifold subspace technique tion update values variables variance wavelet zero