Abstract
Dimensionality reduction is a key technology for face recognition. In this paper, we propose a novel method, called Locality Preserving Fisher Discriminant Analysis (LPFDA), which extends the original Fisher Discriminant Analysis by preserving the locality structure of the data. LPFDA can get a subspace projection matrix by solving a generalized eigenvalue problem. Several experiments are conducted to demonstrate the effectiveness and robustness of our method. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Zhao, X., & Tian, X. (2009). Locality preserving fisher discriminant analysis for face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5754 LNCS, pp. 261–269). https://doi.org/10.1007/978-3-642-04070-2_30
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