Cited By
View all- Isaacs J(2009)Stochastic orthogonal and nonorthogonal subspace basis pursuitProceedings of the 2009 international joint conference on Neural Networks10.5555/1704555.1704630(2496-2501)Online publication date: 14-Jun-2009
Linear subspace analysis methods have been successfully applied to extract features for face recognition. But they are inadequate to represent the complex and nonlinear variations of real face images, such as illumination, facial expression and ...
Manifold learning algorithms mainly focus on discovering the intrinsic low-dimensional manifold embedded in the high-dimensional Euclidean space. Among them, locally linear embedding (LLE) is one of the most promising dimensionality reduction methods. ...
Fisher Linear Discriminant Analysis (FLDA) has been successfully applied to face recognition, which is based on a linear projection from the image space to a low dimensional space by maximizing the between-class scatter and minimizing the within-class ...
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