Quadratic Independent Component Analysis(<Special Section>Nonlinear Theory and its Applications)

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概要

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• 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.

• 社団法人電子情報通信学会の論文
• 2004-09-01

著者

• Nakamura Wakako

  Lab. For Advanced Brain Signal Processing Brain Science Institute Riken

• Theis Fabian

  Lab. For Advanced Brain Signal Processing Brain Science Institute Riken

関連論文

• Quadratic Independent Component Analysis(Nonlinear Theory and its Applications)

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