SIAM Review
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Stepwise Nonparametric Margin Maximum Criterion
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Unsupervised learning of image manifolds by semidefinite programming
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Trace quotient problems revisited
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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In this paper, an efficient feature extraction method, named local sensitive frontier analysis (LSFA), is proposed. LSFA tries to find instances near the crossing of the multi-manifold, which are sensitive to classification, to form the frontier automatically. For each frontier pairwise, those belonging to the same class are applied to construct the sensitive within-class scatter; otherwise, they are applied to form the sensitive between-class scatter. In order to improve the discriminant ability of the instances in low dimensional subspace, a set of optimal projection vectors has been explored to maximize the trace of the sensitive within-class scatter and simultaneously, to minimize the trace of the sensitive between-class scatter. Moreover, with comparisons to some unsupervised methods, such as Unsupervised Discriminant Projection (UDP), as well as some other supervised feature extraction techniques, for example Linear Discriminant Analysis (LDA) and Locality Sensitive Discriminant Analysis (LSDA), the proposed method obtains better performance, which has been validated by the results of the experiments on YALE face database.