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- W2155118899 abstract "We consider the problem of efficiently encoding a signal by transforming it to a new representation whose components are statistically independent. A widely studied linear solution, known as independent component analysis (ICA), exists for the case when the signal is generated as a linear transformation of independent nongaussian sources. Here, we examine a complementary case, in which the source is nongaussian and elliptically symmetric. In this case, no invertible linear transform suffices to decompose the signal into independent components, but we show that a simple nonlinear transformation, which we call radial gaussianization (RG), is able to remove all dependencies. We then examine this methodology in the context of natural image statistics. We first show that distributions of spatially proximal bandpass filter responses are better described as elliptical than as linearly transformed independent sources. Consistent with this, we demonstrate that the reduction in dependency achieved by applying RG to either nearby pairs or blocks of bandpass filter responses is significantly greater than that achieved by ICA. Finally, we show that the RG transformation may be closely approximated by divisive normalization, which has been used to model the nonlinear response properties of visual neurons." @default.
- W2155118899 created "2016-06-24" @default.
- W2155118899 creator A5023752172 @default.
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- W2155118899 date "2009-06-01" @default.
- W2155118899 modified "2023-10-18" @default.
- W2155118899 title "Nonlinear Extraction of Independent Components of Natural Images Using Radial Gaussianization" @default.
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- W2155118899 doi "https://doi.org/10.1162/neco.2009.04-08-773" @default.
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