Spectral Connectivity Analysis

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dc.creatorLee, Ann B.
dc.creatorWasserman, Larry
dc.date2008-11-02
dc.date.accessioned2026-07-07T10:14:47Z
dc.date.available2026-07-07T10:14:47Z
dc.descriptionSpectral kernel methods are techniques for transforming data into a coordinate system that efficiently reveals the geometric structure - in particular, the "connectivity" - of the data. These methods depend on certain tuning parameters. We analyze the dependence of the method on these tuning parameters. We focus on one particular technique - diffusion maps - but our analysis can be used for other methods as well. We identify the population quantities implicitly being estimated, we explain how these methods relate to classical kernel smoothing and we define an appropriate risk function for analyzing the estimators. We also show that, in some cases, fast rates of convergence are possible even in high dimensions.
dc.identifierhttps://arxiv.org/abs/0811.0121
dc.identifierhttp://arxiv.org/abs/0811.0121
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/173000
dc.subjectMethodology
dc.titleSpectral Connectivity Analysis
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