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10

ICCV
1999
IEEE

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Principal Manifolds and Bayesian Subspaces for Visual Recognition

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Principal Manifolds and Bayesian Subspaces for Visual Recognition

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We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Three techniques: Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Nonlinear PCA (NLPCA) are examined and tested in a visual recognition experiment using a large gallery of facial images from the

Baback Moghaddam

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Computer Vision | Electric Research Laboratories | ICCV 1999 | Mitsubishi Electric Research | Principal Manifold |

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Added	03 Aug 2010
Updated	03 Aug 2010
Type	Conference
Year	1999
Where	ICCV
Authors	Baback Moghaddam

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