Quadratic independent component analysis
نویسندگان
چکیده
The transformation of a data set using a second-order polynomial mapping to find statistically independent components is considered (quadratic independent component analysis or ICA). Based on overdetermined linear ICA, an algorithm together with separability conditions are given via linearization reduction. The linearization is achieved using a higher dimensional embedding defined by the linear parametrization of the monomials, which can also be applied for higher-order polynomials. The paper finishes with simulations for artificial data and natural images. key words: nonlinear independent component analysis, quadratic forms, nonlinear blind source separation, overdetermined blind source separation, natural images
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