Embedding new data points for manifold learning via coordinate propagation
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Multiview Metric Learning with Global Consistency and Local Smoothness
ACM Transactions on Intelligent Systems and Technology (TIST)
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This paper presents a novel scheme for manifold learning. Different from the previous work reducing data to Euclidean space which cannot handle the looped manifold well, we map the scattered data to its intrinsic parameter manifold by semi-supervised learning. Given a set of partially labeled points, the map to a specified parameter manifold is computed by an iterative neighborhood average method called Anchor Points Diffusion procedure (APD). We explore this idea on the most frequently used close-formed manifolds, Stiefel manifolds whose special cases include hyper sphere and orthogonal group. The experiments show that APD can recover the underlying intrinsic parameters of points on scattered data manifold successfully.