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| Discriminant Improved Local Tangent Space Alignment with Application to Face Recognition |
| Qiang Zhang, Rui Wang, Yunze Cai, Xiaoming Xu |
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Abstract Improved Local Tangent Space Alignment (ILTSA) is a recent nonlinear
dimensionality reduction method but there exists the out-of-sample
problem. In this paper, based on linearization and discriminant
extension of ILTSA, a novel feature extraction method named
Discriminant Improved Local Tangent Space Alignment (DILTSA) is
proposed. Based on maximum neighborhood margin criterion and ILTSA,
DILTSA can preserve both local within-class and between-class
geometry structures. Experimental results on ORL and UMIST face
databases demonstrate the effectiveness of the proposed face
recognition method.
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