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In this paper, we propose a novel stochastic framework for unsupervised manifold learning. The latent variables are introduced, and the latent processes are assumed to characterize the pairwise relations of points over a high dimensional and a low dimensional space. The elements in the embedding space are obtained by minimizing the divergence between the latent processes over the two spaces. Different...
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