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Research - M. Alex O. Vasilescu
In the context of facial image ensembles, we demonstrate that the statistical regularities learned by MICA capture information that improves automatic face recognition. "Multilinear (Tensor) ICA and Dimensionality Reduction", M.A.O. Vasilescu, D. Terzopoulos, Proc. 7th International Conference on Independent Component Analysis and Signal ......
CiteSeerX — Multilinear Independent Components Analysis
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Independent Components Analysis (ICA) maximizes the statistical independence of the representational components of a training image ensemble, but it cannot distinguish between the different factors, or modes, inherent to image formation, including scene structure, illumination, and imaging.
Multilinear independent components analysis - IEEE ...
Abstract: Independent components analysis (ICA) maximizes the statistical independence of the representational components of a training image ensemble, but it cannot distinguish between the different factors, or modes, inherent to image formation, including scene structure, illumination, and imaging. We introduce a nonlinear, multifactor model that generalizes ICA.
Multilinear Independent Components Analysis
Multilinear Independent Components Analysis M. Alex O. Vasilescu1,2 and Demetri Terzopoulos2,1 1Department of Computer Science, University of Toronto, Toronto ON M5S 3G4, Canada 2Courant Institute of Mathematical Sciences, New York University, New York, NY 10003, USA Abstract IndependentComponentsAnalysis(ICA)maximizesthesta-tistical independence of the representational components of...
A Multifactor Extension of Linear Discriminant Analysis ...
Linear Discriminant Analysis (LDA) and Multilinear Principal Component Analysis (MPCA) are leading subspace methods for achieving dimension reduction based on supervised learning. Both LDA and MPCA use class labels of data samples to calculate subspaces onto which these samples are projected. Furthermore, both methods have been successfully applied to face recognition....
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