Local procrustes for manifold embedding: A measure of embedding quality and embedding algorithms

Yair Goldberg*, Ya'Acov Ritov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

We present the Procrustes measure, a novel measure based on Procrustes rotation that enables quantitative comparison of the output of manifold-based embedding algorithms such as LLE (Roweis and Saul, Science 290(5500), 2323-2326, 2000) and Isomap (Tenenbaum et al., Science 290(5500), 2319-2323, 2000). The measure also serves as a natural tool when choosing dimension-reduction parameters. We also present two novel dimension-reduction techniques that attempt to minimize the suggested measure, and compare the results of these techniques to the results of existing algorithms. Finally, we suggest a simple iterative method that can be used to improve the output of existing algorithms.

Original languageEnglish
Pages (from-to)1-25
Number of pages25
JournalMachine Learning
Volume77
Issue number1
DOIs
StatePublished - Oct 2009

Keywords

  • Dimension reducing
  • Local PCA
  • Manifold learning
  • Procrustes analysis
  • Simulated annealing

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