Nonlinear Dimensionality Reduction by Minimum Curvilinearity for Unsupervised Discovery of Patterns in Multidimensional Proteomic Data.

Abstract

Dimensionality reduction is largely and successfully employed for the visualization and discrimination of patterns, hidden in multidimensional proteomics datasets. Principal component analysis (PCA), which is the preferred approach for linear dimensionality reduction, may present serious limitations, in particular when samples are nonlinearly related, as… (More)
DOI: 10.1007/978-1-4939-3255-9_16

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Cite this paper

@article{Alessio2016NonlinearDR, title={Nonlinear Dimensionality Reduction by Minimum Curvilinearity for Unsupervised Discovery of Patterns in Multidimensional Proteomic Data.}, author={Massimo Alessio and Carlo Vittorio Cannistraci}, journal={Methods in molecular biology}, year={2016}, volume={1384}, pages={289-98} }