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We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and nonlinear Kernel PCA (KPCA) are examined and tested in a visual recognition experiment using 1,800+ facial images from the "FERET"database....

https://dl.acm.org/doi/10.1109/TPAMI.2002.1008384

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