Abstract
Blind source separation (BSS) and related methods, e.g., ICA are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to psychology and neuroscience. The recent trends in BSS is to consider problems in the framework of probabilistic generative and tree structured graphical models and exploit a priori knowledge about true nature and structure of latent (hidden) variables or sources such as statistical independence, spatio-temporal decorrelation, sparseness, smoothness or linear predictability. The goal of BSS can be considered as estimation of sources and parameters of a mixing system or more generally as finding a new reduced or compressed representation for the observed (sensor) data that can be interpreted as physically meaningful coding or blind source extraction. The key issue is to find a such transformation or coding (linear or nonlinear) which has true physical meaning and interpretation. In this paper, we briefly review some promising linear models and approaches to blind source separation and extraction using various criteria and assumptions.
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Cichocki, A., Zurada, J.M. (2004). Blind Signal Separation and Extraction: Recent Trends, Future Perspectives, and Applications. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_4
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DOI: https://doi.org/10.1007/978-3-540-24844-6_4
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