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- W91978093 abstract "We present an unsupervised nearest neighbors (UNN) variant for continuous latent spaces that allows to embed patterns in different submanifolds. The problem to simultaneously assign patterns to models and learn the embeddings can be very challenging, as the manifolds may lie closely to each other and can have different dimensions and arbitrary curvature. The UNN-based submanifold learning approach (SL-UNN) that is proposed in this paper combines a fast constructive K-means variant with the UNN manifold learning approach. The resulting speedy approach depends on only few parameters, i.e., a distance threshold to allow the definition of new clusters and the usual UNN parameters. Extensions of SL-UNN are able to automatically determine parameter of each submanifold based on the data space reconstruction error." @default.
- W91978093 created "2016-06-24" @default.
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- W91978093 date "2013-01-01" @default.
- W91978093 modified "2023-09-23" @default.
- W91978093 title "Fast Submanifold Learning with Unsupervised Nearest Neighbors" @default.
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- W91978093 doi "https://doi.org/10.1007/978-3-642-37213-1_33" @default.
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