Nonlinear dimensionality reduction by locally linear embedding.

  title={Nonlinear dimensionality reduction by locally linear embedding.},
  author={Sam T. Roweis and Lawrence K. Saul},
  volume={290 5500},
Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction… CONTINUE READING
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