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. © 2009 Springer Science+Business Media, LLC.
Author supplied keywords
Cite
CITATION STYLE
Goldberg, Y., & Ritov, Y. (2009). Local procrustes for manifold embedding: A measure of embedding quality and embedding algorithms. Machine Learning, 77(1), 1–25. https://doi.org/10.1007/s10994-009-5107-9
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.