Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Optimization Algorithms on Matrix Manifolds
Optimization Algorithms on Matrix Manifolds
Joint blind source separation by multiset canonical correlation analysis
IEEE Transactions on Signal Processing
Source extraction by maximizing the variance in the conditional distribution tails
IEEE Transactions on Signal Processing
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Stability of independent vector analysis
Signal Processing
Approximate Joint Singular Value Decomposition of an Asymmetric Rectangular Matrix Set
IEEE Transactions on Signal Processing
Blind Source Separation Exploiting Higher-Order Frequency Dependencies
IEEE Transactions on Audio, Speech, and Language Processing
Approach and applications of constrained ICA
IEEE Transactions on Neural Networks
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In this paper we will study the advantages of Jacobi iterations to solve the problem of Canonical Dependence Analysis. Canonical Dependence Analysis can be seen as an extension of the Canonical Correlation Analysis where correlation measures are replaced by measures of higher order statistical dependencies. We will show the benefits of choosing an algorithm that exploits the manifold structure on which the optimisation problem can be formulated and contrast our results with the joint blind source separation algorithm that optimises the criterion in its ambient space. A major advantage of the proposed algorithm is the capability of identifying a linear mixture when multiple observation sets are available containing variables that are linearly dependent between the sets, independent within the sets and contaminated with non-Gaussian independent noise. Performance analysis reveals at least linear convergence speed as a function of the number of sweeps.