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

作者:Yair Goldberg, Ya’acov Ritov

摘要

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.

论文关键词:Dimension reducing, Manifold learning, Procrustes analysis, Local PCA, Simulated annealing

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论文官网地址:https://doi.org/10.1007/s10994-009-5107-9