Weighted locally linear embedding for dimension reduction

DOI10.1016/j.patcog.2008.08.024zbMath1178.68453OpenAlexW2038622192WikidataQ57527483 ScholiaQ57527483MaRDI QIDQ1010087

J. Martínez

Publication date: 3 April 2009

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2008.08.024

zbMATH Keywords

feature extractionmanifold learningnonlinear dimensionality reductionlocally linear embedding

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (4)

On the regularized Laplacian eigenmaps ⋮ Geometrically local embedding in manifolds for dimension reduction ⋮ Nonlinear embedding preserving multiple local-linearities ⋮ Constructing \(L_1\)-graphs for subspace learning via recurrent neural networks

Uses Software

• JDQR
• JDQZ
• KPCA plus LDA

Cites Work

• Reconstruction and analysis of multi-pose face images based on nonlinear dimensionality reduction.
• Principal component analysis.
• Distance metric learning by knowledge embedding
• RBF-based neurodynamic nearest neighbor classification in real pattern space
• 10.1162/153244304322972667
• An Optimal Global Nearest Neighbor Metric
• The optimal distance measure for nearest neighbor classification
• Jacobi--Davidson Style QR and QZ Algorithms for the Reduction of Matrix Pencils
• Optimization of k nearest neighbor density estimates
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Unnamed Item
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