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- W2068242198 abstract "In face recognition, Principal Component Analysis (PCA) is often used to extract a low dimensional face representation based on the eigenvector of the face image autocorrelation matrix. Kernel Principal Component Analysis (Kernel PCA) has recently been proposed as a non-linear extension of PCA. While PCA is able to discover and represent linearly embedded manifolds, Kernel PCA can extract low dimensional non-linearly embedded manifolds from data, thus providing a more suitable recognition by a classifier. We provide experimental evidence which show that Kernel PCA performs better than PCA on the ATT Face Dataset when both are used with a lienar Support Vecter Machine Classifier. Philippine Computing Journal 1(1) 2006 27-30" @default.
- W2068242198 created "2016-06-24" @default.
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- W2068242198 date "2008-05-22" @default.
- W2068242198 modified "2023-09-26" @default.
- W2068242198 title "Recognizing Faces Using Kernel Eigenfaces" @default.
- W2068242198 doi "https://doi.org/10.3860/pcj.v1i1.214" @default.
- W2068242198 hasPublicationYear "2008" @default.
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