Some real problems, such as image recognition or the analysis of gene expression data, involve the observation of a very large number of variables on a few units. In such a context conventional classification methods are difficult to employ both from analytical and interpretative points of view. In this paper, a solution based on locally linear embedding (LLE) for supervised classification is proposed and applied to the analysis of some gene expression datasets.

Locally linear embedding for nonlinear dimension reduction in classification problems: an application to gene expression data

PILLATI, MARILENA;VIROLI, CINZIA
2005

Abstract

Some real problems, such as image recognition or the analysis of gene expression data, involve the observation of a very large number of variables on a few units. In such a context conventional classification methods are difficult to employ both from analytical and interpretative points of view. In this paper, a solution based on locally linear embedding (LLE) for supervised classification is proposed and applied to the analysis of some gene expression datasets.
2005
M. Pillati; C. Viroli
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/25949
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