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

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dc.creatorGoldberg, Y.
dc.creatorRitov, Y.
dc.date2008-06-16
dc.date.accessioned2026-07-07T09:44:58Z
dc.date.available2026-07-07T09:44:58Z
dc.descriptionWe 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.
dc.descriptionSubmitted to Journal of Machine Learning
dc.identifierhttps://arxiv.org/abs/0806.2669
dc.identifierhttp://arxiv.org/abs/0806.2669
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/163060
dc.subjectMachine Learning
dc.titleLocal Procrustes for Manifold Embedding: A Measure of Embedding Quality and Embedding Algorithms
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