Dimensionality reduction based on kCCC and manifold learning

DOI10.1007/S10851-021-01031-5OpenAlexW3172563353MaRDI QIDQ2051142

Zhengming Ma, Gengshi Huang, Tianshi Luo

Publication date: 24 November 2021

Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10851-021-01031-5

zbMATH Keywords

dimensionality reductionmanifold learningstatistical machine learning

Mathematics Subject Classification ID

Computer science (68-XX) Information and communication theory, circuits (94-XX)

Cites Work

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• A comparative review of dimension reduction methods in approximate Bayesian computation
• $\ell _{2,p}$ -Norm Based PCA for Image Recognition
• Local Deep-Feature Alignment for Unsupervised Dimension Reduction
• Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data

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