Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spherical averages and applications to spherical splines and interpolation
ACM Transactions on Graphics (TOG)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised sub-manifold discriminant analysis
Pattern Recognition Letters
Fast manifold learning based on riemannian normal coordinates
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Nonlinear Discriminant Analysis on Embedded Manifold
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Advances in matrix manifolds for computer vision
Image and Vision Computing
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In this paper, we present a geodesic discriminant analysis (GDA) algorithm, which generalize linear discriminant analysis (LDA) in linear manifold space to curved Riemannian manifold space, with the aid of Riemannian logarithmic map. Compared with LDA, GDA is more suitable to deal with data that lie on curved manifold. We show that GDA is the generalization of LDA, and LDA is the special case of GDA: GDA equals to the data-centralized LDA when the underlying manifold is a linear manfold. Experimental results on facial needle-map data show the superiority of GDA over LDA when data lie on curved manifold.