Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
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In this paper, a novel linear subspace learning algorithm called orthogonal discriminant linear local tangent space alignment (ODLLTSA) is proposed. Derived from linear local tangent space alignment (LLTSA), ODLLTSA not only inherits the advantages of LLTSA which uses linear local tangent space as a representation of the local geometry to preserve the local structure, but also makes full use of class information to improve recognition power, solves the optimal subspace by spectral regression, and then orthogonalizes the subspace. Experimental results on standard face databases demonstrate the effectiveness of the proposed algorithm.