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 language | English |
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Pages (from-to) | 1-25 |
Number of pages | 25 |
Journal | Machine Learning |
Volume | 77 |
Issue number | 1 |
DOIs | |
State | Published - Oct 2009 |
Keywords
- Dimension reducing
- Local PCA
- Manifold learning
- Procrustes analysis
- Simulated annealing