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Following the intuition that the image variation of faces can be effectively modeled by low dimensional linear spaces, we propose a novel linear subspace learning method for face analysis in the framework of graph embedding model, called semi-supervised graph embedding (SGE). This algorithm builds an adjacency graph which can best respect the geometry structure inferred from the must-link pairwise...
Subspace methods have been successfully applied to face recognition tasks. It is well-studied in both unsupervised learning and supervised learning, such as Eigenface and Fisherface. In practice, besides abundant unlabeled examples, domain knowledge in the form of pairwise constraints is commonly available, which specifies whether a pair of instances belong to the same class or different classes....
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