Stochastic Neighborhood Embedding
摘要
LLE is widely appreciated as an effective dimension-reduction tool. It fares less well, however, in situations of very high-dimensional data sets. Moreover, an essential assumption of LLE is the presence of a single smooth manifold in the data. If the data contain multiple manifolds or if there are regions, where the density of observed data varies a lot, LLE has performance weaknesses.