Local Procrustes for Manifold Embedding: A Measure of Embedding Quality and Embedding Algorithms

Autor:	Goldberg, Y., Ritov, Y.
Rok vydání:	2008
Předmět:	Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Submitted to Journal of Machine Learning
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/0806.2669 Zobrazit plný text záznamu View this record from Arxiv