Dimensionality reduction: an interpretation from manifold regularization perspective

DOI10.1016/j.ins.2014.03.011zbMath1354.68222OpenAlexW1990517601MaRDI QIDQ506149

Mingyu Fan, Hong Qiao, Nannan Gu, Bo Zhang

Publication date: 31 January 2017

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2014.03.011

zbMATH Keywords

dimensionality reductionmanifold learningmanifold regularizationfeature mappingout-of-sample extrapolation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Estimating dynamical dimensions from noisy observations

Uses Software

• t-SNE

Cites Work

• Manifold elastic net: a unified framework for sparse dimension reduction
• Classification of gene-expression data: the manifold-based metric learning way
• Principal component analysis.
• Kernel dimension reduction in regression
• Multimodal biometrics using geometry preserving projections
• Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
• Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
• A General Framework for Dimensionality-Reducing Data Visualization Mapping
• 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
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item