Blind source separation: new tools for extraction of source signals and denoising

Blind source separation (BSS) and related methods such as independent component analysis (ICA) and their extensions or sparse component analysis (SCA) refers to wide class of problems in signal and image processing, when one needs to extract the underlying sources from a set of mixture. The goal of BSS can be considered as estimation of true physical sources and parameters of a mixing system, while objective of generalized component analysis (GCA) is finding a new reduced or hierarchical and structured representation for the observed (sensor) multidimensional data that can be interpreted as physically meaningful coding or blind signal decompositions. These methods are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. The recent trends in blind source separation and generalized component analysis is to consider problems in the framework of matrix factorization or more general signals decomposition with probabilistic generative and tree structured graphical models and exploit some priori knowledge about true nature and structure of latent (hidden) components or sources such as spatio-temporal decorrelation, statistical independence, sparsity, nonnegativity, smoothness or lowest possible complexity. The key issue is to find a such transformation or coding which has true physical meaning and interpretation. In this paper we discuss some promising approaches and algorithms for BSS/GCA, especially for ICA and SCA in order to analyze, enhance, perform feature extraction, removing artifacts and denoising of multi-modal, multi-sensory data.

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