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Data observations that lie on a manifold can be approximated by a collection of overlapping local patches, the alignment of which in a low-dimensional euclidean space provides an embedding of the data. This paper describes an embedding method using classical multidimensional scaling as a local model based on the fact that a manifold locally resembles a euclidean space. A set of overlapping neighborhoods...
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