Grouping and dimensionality reduction by locally linear embedding
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TL;DR
A variant of LLE that can simultaneously group the data and calculate local embedding of each group is studied, and an estimate for the upper bound on the intrinsic dimension of the data set is obtained automatically.
Abstract
Locally Linear Embedding (LLE) 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.