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- W2046408041 abstract "We have developed new dimensionality reduction methods, extended from locality preserving (LPP), to estimate age using facial images. LPP seeks a linear transformation matrix such that optimally preserves the neighborhood structure of the data. Our focus has been on expanding LPP by making use of label information. Specifically, one of our ideas is to assign weights only to the data with close labels. A local scaling method is used for each to compute the LPP affinity matrix. Another idea is to assign large weights to two samples with close labels, i.e., close ages. By doing this, label information for original data (i.e., age information) can be preserved. We thus call this class distance weighted linear preserving projection (CDLPP). Experimental results on a large database showed that CDLPP has more discriminative power than conventional methods such as PCA and LPP." @default.
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- W2046408041 date "2008-01-01" @default.
- W2046408041 modified "2023-10-05" @default.
- W2046408041 title "Class Distance Weighted Locality Preserving Projection for Automatic Age Estimation" @default.
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- W2046408041 doi "https://doi.org/10.1109/btas.2008.4699380" @default.
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