Grouping and dimensionality reduction by locally linear embedding

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Marzia Polito, Pietro Perona



Locally Linear Embedding is an elegant nonlinear dimensionality-reduction technique recently introduced by Roweis and Saul [2]. It fails when the data is divided into separate groups. We study a variant of LLE that can simultaneously group the data and calculate local embedding of each group. An estimate for the upper bound on the intrinsic dimension of the data set is obtained automatically.