Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings

Abstract

The recovery of the intrinsic geometric structures of data collections is an important problem in data analysis. Supervised extensions of several manifold learning approaches have been proposed in the recent years. Meanwhile, existing methods primarily focus on the embedding of the training data, and the generalization of the embedding to initially unseen… (More)

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