Separation of sources consists of recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori information on the mixing matrix is available:… (More)

|Source separation consists in recovering a set of independent signals when only mixtures with unknown coef-cients are observed. This paper introduces a class of adap-tive algorithms for source… (More)

This article considers high-order measures of independence for the independent component analysis problem and discusses the class of Jacobi algorithms for their optimization. Several implementations… (More)

Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis that aim to recover unobserved signals or “sources” from observed… (More)

This discussion paper proposes to generalize the notion of I dependent Component Analysis (ICA) to the notion of Multidimensional Independent Component Analysis (MICA). We start from the ICA or blind… (More)

Simultaneous diagonalization of several matrices can be implemented by a Jacobi-like technique. This note gives the required Jacobi angles in close form. Key words, simultaneous diagonalization,… (More)

Most ICA algorithms are based on a model of stationary sources. This paper considers exploiting the (possible ) non-stationarity of the sources to achieve separation. We i ntroduce two objective… (More)

This communication presents a simple algebraic method for the extraction of independent components in multidimensional data. Since statistical independence is a much stronger property than… (More)