Principal Manifolds and Bayesian Subspaces for Visual Recognition

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

• Type: Conference
• Year: 1999
• Where: ICCV
• Authors: Baback Moghaddam
• Keywords: Computer Vision, Electric Research Laboratories, ICCV 1999, Mitsubishi Electric Research, Principal Manifold