We present independent slow feature analysis as a new method for nonlinear blind source separation. It circumvents the indeterminacy of nonlinear independent component analysis by ...
Abstract. In this paper we examine how the activation of one independent component analysis (ICA) feature changes first and second order statistics of other independent components...
Post-nonlinear (PNL) independent component analysis (ICA) is a generalisation of ICA where the observations are assumed to have been generated from independent sources by linear mi...
— Multidimensional or group independent component analysis describes the task of transforming a multivariate observed sensor signal such that groups of the transformed signal com...
Abstract. This paper derives a new algorithm that performs independent component analysis (ICA) by optimizing the contrast function of the RADICAL algorithm. The core idea of the p...
A common problem in independent component analysis after prewhitening is to optimize some contrast on the orthogonal or unitary group. A popular approach is to optimize the contras...
We consider Independent Component Analysis (ICA) for the case of binary sources, where addition has the meaning of the boolean “Exclusive Or” (XOR) operation. Thus, each mixtur...
Abstract. Renyi’s entropy-based criterion has been proposed as an objective function for independent component analysis because of its relationship with Shannon’s entropy and i...
Abstract. In contrast to the traditional hypothesis-driven methods, independent component analysis (ICA) is commonly used in functional magnetic resonance imaging (fMRI) studies to...
FastICA is arguably one of the most widespread methods for independent component analysis. We focus on its deflation-based implementation, where the independent components are ext...