Local Procrustes for Manifold Embedding: A Measure of Embedding Quality and Embedding Algorithms
Machine Learning
2008-06-18 v1
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, 2000) and Isomap (Tenenbaum et al, 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.
Keywords
Cite
@article{arxiv.0806.2669,
title = {Local Procrustes for Manifold Embedding: A Measure of Embedding Quality and Embedding Algorithms},
author = {Y. Goldberg and Y. Ritov},
journal= {arXiv preprint arXiv:0806.2669},
year = {2008}
}
Comments
Submitted to Journal of Machine Learning