A metric multidimensional scaling-based nonlinear manifold learning approach for unsupervised data reduction

DOI10.1155/2008/862015zbMath1184.68388OpenAlexW2070671935WikidataQ59216038 ScholiaQ59216038MaRDI QIDQ966949

M. Brucher, Fabrice Heitz, Christian Heinrich, Jean-Paul Armspach

Publication date: 24 April 2010

Published in: EURASIP Journal on Advances in Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2008/862015

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• COIL-20

Cites Work

• Multivariate adaptive regression splines
• Linear manifold clustering in high dimensional spaces by stochastic search
• Diffusion maps
• Adaptive Principal Surfaces
• On Fréchet means in simplex shape spaces
• Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
• Principal Curves
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• A self-organizing principle for learning nonlinear manifolds
• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data

This page was built for publication: A metric multidimensional scaling-based nonlinear manifold learning approach for unsupervised data reduction