Local multidimensional scaling

DOI10.1016/j.neunet.2006.05.014zbMath1102.68601OpenAlexW2043508111WikidataQ51939442 ScholiaQ51939442MaRDI QIDQ2506526

Samuel Kaski, Jarkko Venna

Publication date: 10 October 2006

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2006.05.014

zbMATH Keywords

information visualizationgene expressionnonlinear dimensionality reductionmanifold extractionmulti-dimensional scaling (MDS)non-linear projection

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (10)

Parametric UMAP Embeddings for Representation and Semisupervised Learning ⋮ Dimension reduction and construction of feature space for image pattern recognition ⋮ CORRELATION-BASED MULTIDIMENSIONAL SCALING FOR UNSUPERVISED SUBSPACE LEARNING ⋮ Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition ⋮ Neural manifold analysis of brain circuit dynamics in health and disease ⋮ Local Procrustes for manifold embedding: a measure of embedding quality and embedding algorithms ⋮ Extreme-value-theoretic estimation of local intrinsic dimensionality ⋮ Topology representing networks for the visualization of manifolds ⋮ The Notion of Pre-aggregation Function ⋮ A technique for characterising feature size and quality of manifolds

Cites Work

• Modern multidimensional scaling: theory and applications
• Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
• Multidimensional scaling. I: Theory and method
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Some distance properties of latent root and vector methods used in multivariate analysis
• Self-organizing maps.
• Unnamed Item

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