Using state space differential geometry for nonlinear blind source separation
نویسندگان
چکیده
منابع مشابه
Using State Space Differential Geometry for Nonlinear Blind Source Separation
Given a time series of multicomponent measurements of an evolving stimulus, nonlinear blind source separation (BSS) seeks to find a “source” time series, comprised of statistically independent combinations of the measured components. In this paper, we seek a source time series with local velocity cross correlations that vanish everywhere in stimulus state space. However, in an earlier paper the...
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We propose a new method for learning a nonlinear dynamical state-space model in unsupervised manner. The proposed method can be viewed as a nonlinear dynamic generalization of standard linear blind source separation (BSS) or independent component analysis (ICA). Using ensemble learning, the method finds a nonlinear dynamical process which can explain the observations. The nonlinearities are mod...
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Blind source separation (BSS) and the closely related Independent Component Analysis (ICA) have recently drawn a lot of attention in unsupervised neural learning and statistical signal processing 1, 2, 3]. In the basic linear case, ICA and BSS use the same data model x(t) = As(t) = m X i=1 s i (t)a i : (1) Here x(t) is the n-dimensional t:th data vector, s 1 (t); : : : ; s m (t) are the respect...
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This article proposes the fusion of two important paradigms, Genetic Algorithms and the Blind Separation of Sources (GABSS). Although the topic of BSS, by means of various techniques, including ICA, PCA and neural networks, has been amply discussed in the literature, to date the possibility of using genetic algorithms has not been explored. However, in nonlinear mixtures, optimisation of the sy...
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ژورنال
عنوان ژورنال: Journal of Applied Physics
سال: 2008
ISSN: 0021-8979,1089-7550
DOI: 10.1063/1.2826943