A metric multidimensional scaling-based nonlinear manifold learning approach for unsupervised data reduction
type: Item
instance of: scholarly article
title: A metric multidimensional scaling-based nonlinear manifold learning approach for unsupervised data reduction
zbMATH Open document ID: 1184.68388
DOI: 10.1155/2008/862015
author: M. Brucher
author: Christian Heinrich
author: F. Heitz
author: Jean-Paul Armspach
published in: EURASIP Journal on Advances in Signal Processing
publication date: 2010-04-24T00:00:00Z
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 5701005
describes a project that uses: COIL-20
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2008/862015
OpenAlex ID: W2070671935
Wikidata QID: Q59216038
cites work: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
cites work: Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
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cites work: Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
cites work: Principal Curves
cites work: Adaptive Principal Surfaces
cites work: A self-organizing principle for learning nonlinear manifolds
cites work: Multivariate adaptive regression splines
cites work: Linear manifold clustering in high dimensional spaces by stochastic search
cites work: On Fréchet means in simplex shape spaces
DBLP publication ID: journals/ejasp/BrucherHHA08
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description: scientific article; zbMATH DE number 5701005