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Generalized Unsupervised Manifold Alignment
2014
Neural Information Processing Systems
In this paper, we propose a Generalized Unsupervised Manifold Alignment (GU-MA) method to build the connections between different but correlated datasets without any known correspondences. Based on the assumption that datasets of the same theme usually have similar manifold structures, GUMA is formulated into an explicit integer optimization problem considering the structure matching and preserving criteria, as well as the feature comparability of the corresponding points in the mutual
dblp:conf/nips/CuiCSC14
fatcat:vskds52pjngkln7dzdzagjmt7i