Abstract
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.
Original language | English |
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Pages (from-to) | 2355-2363 |
Number of pages | 9 |
Journal | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |
Volume | E87-A |
Issue number | 9 |
State | Published - Sep 2004 |
Externally published | Yes |
Keywords
- Natural images
- Nonlinear blind source separation
- Nonlinear independent component analysis
- Overdetermined blind source separation
- Quadratic forms