Robust kernel Isomap

DOI10.1016/j.patcog.2006.04.025zbMath1118.68188OpenAlexW1994443280WikidataQ126087168 ScholiaQ126087168MaRDI QIDQ856442

Seungjin Choi, Heeyoul Choi

Publication date: 7 December 2006

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2006.04.025

zbMATH Keywords

multidimensional scaling (MDS)manifold learningIsomapnonlinear dimensionality reductionkernel PCA

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (10)

Geodesic-based distance reveals nonlinear topological features in neural activity from mouse visual cortex ⋮ Nonlinear manifold learning for meshfree finite deformation thin-shell analysis ⋮ Nonlinear dimensionality reduction by topologically constrained isometric embedding ⋮ Regularized discriminant entropy analysis ⋮ Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding ⋮ Person-independent head pose estimation using biased manifold embedding ⋮ Membership-degree preserving discriminant analysis with applications to face recognition ⋮ Parallel Transport Unfolding: A Connection-Based Manifold Learning Approach ⋮ A Convex Matrix Optimization for the Additive Constant Problem in Multidimensional Scaling with Application to Locally Linear Embedding ⋮ Nonlinear Level Set Learning for Function Approximation on Sparse Data with Applications to Parametric Differential Equations

Uses Software

• UCI-ml
• Isomap

Cites Work

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• The analytical solution of the additive constant problem
• 10.1162/153244304322972667
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• On a connection between kernel PCA and metric multidimensional scaling

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