Titlebook: Independent Component Analysis and Blind Signal Separation; 6th International Co Justinian Rosca,Deniz Erdogmus,Simon Haykin Conference pro

AWE

Fast Kernel Density Independent Component Analysisrt ICA algorithms, simulation studies show that KDICA is promising for practical usages due to its computational efficiency as well as statistical efficiency. Some statistical properties of KDICA are analyzed.

伴隨而來

Estimating the Information Potential with the Fast Gauss Transformonverges to the actual entropy value rapidly with increasing order p unlike the Stochastic Information Gradient, the present O(.) approximation to reduce the computational complexity in ITL. We test the performance of these FGT methods on System Identification with encouraging results.

modifier

Differential Fast Fixed-Point BSS for Underdetermined Linear Instantaneous Mixturess therefore based on a differential sphering, followed by the optimization of the differential kurtosis that we introduce in this paper. Experimental tests show that this differential method is much more robust to noise than standard FastICA.

incite

Equivariant Algorithms for Estimating the Strong-Uncorrelating Transform in Complex Independent Compoduces the strong uncorrelating transform when the circularity coefficients of the sources are distinct and positive. Simulations show the efficacy of the approach in a source clustering task for wireless communications.

Nefarious

K-EVD Clustering and Its Applications to Sparse Component Analysisted. We have applied the proposed approach for blind source separation. The simulations show that the proposed algorithm is reliable and of high accuracy, even when the number of sources is unknown and/or overestimated.

刪除

New Permutation Algorithms for Causal Discovery Using ICAber of variables considered is large. Thus we extend the applicability of the method to data sets with tens of variables or more. Experiments confirm the performance of the proposed algorithms, implemented as part of the latest version of our freely available Matlab/Octave LiNGAM package.

大漩渦

Simple LU and QR Based Non-orthogonal Matrix Joint Diagonalizationlem by a sequence of simple one dimensional minimization problems. In addition, a new scale-invariant cost function for non-orthogonal joint diagonalization is employed. These algorithms are step-size free. Numerical simulations demonstrate the efficiency of the methods.

Introduction

BINGE

Model Structure Selection in Convolutive Mixtureson in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data.

Vo2-Max

An EM Method for Spatio-temporal Blind Source Separation Using an AR-MOG Source Model methods the proposed algorithm takes into account both spatial and temporal information, optimization is performed using the Expectation-Maximization method, and the source model is learned along with the demixing parameters.