Out-of-Sample Embedding for Manifold Learning Applied to Face Recognition

CVPR 2013
Pages: 862 - 868
Published: Jun 23, 2013
Abstract
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...
Paper Details
Title
Out-of-Sample Embedding for Manifold Learning Applied to Face Recognition
DOI
Published Date
Jun 23, 2013
Journal
Pages
862 - 868
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