nips nips2002 nips2002-97 nips2002-97-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Vin D. Silva, Joshua B. Tenenbaum
Abstract: Recently proposed algorithms for nonlinear dimensionality reduction fall broadly into two categories which have different advantages and disadvantages: global (Isomap [1]), and local (Locally Linear Embedding [2], Laplacian Eigenmaps [3]). We present two variants of Isomap which combine the advantages of the global approach with what have previously been exclusive advantages of local methods: computational sparsity and the ability to invert conformal maps.
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