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Out-of-Sample Embedding for Manifold Learning Applied to Face Recognition
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
Manifold learning techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed schemes were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only
doi:10.1109/cvprw.2013.127
dblp:conf/cvpr/DornaikaR13
fatcat:7embzgl2wbcntc3nkedihio3ua