Learning gradients on manifolds

DOI10.3150/09-BEJ206zbMath1200.62070arXiv1002.4283WikidataQ56906130 ScholiaQ56906130MaRDI QIDQ605040

Ding-Xuan Zhou, Qiang Wu, Sayan Mukherjee

Publication date: 12 November 2010

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1002.4283

zbMATH Keywords

classificationshrinkage estimatorTikhonov regularizationregressiongene expression datafeature selectionmanifold learning

Mathematics Subject Classification ID

Multivariate analysis (62H99) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Local differential geometry (53B99)

Related Items

Normal estimation on manifolds by gradient learning ⋮ Error analysis on Hérmite learning with gradient data ⋮ Learning Subspaces of Different Dimensions ⋮ ERM scheme for quantile regression ⋮ Keypoints and local descriptors of scalar functions on 2D manifolds ⋮ Learning gradients via an early stopping gradient descent method ⋮ Estimating variable structure and dependence in multitask learning via gradients ⋮ Structure learning via unstructured kernel-based M-estimation ⋮ Learning sparse gradients for variable selection and dimension reduction ⋮ Refined Generalization Bounds of Gradient Learning over Reproducing Kernel Hilbert Spaces ⋮ Local Linear Regression on Manifolds and Its Geometric Interpretation ⋮ Learning the coordinate gradients ⋮ Unnamed Item ⋮ Performance analysis of the LapRSSLG algorithm in learning theory ⋮ The topology of probability distributions on manifolds

Uses Software

• PDCO

Cites Work

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• Atomic Decomposition by Basis Pursuit
• An Adaptive Estimation of Dimension Reduction Space
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
• Theory of Reproducing Kernels
• Gene selection for cancer classification using support vector machines

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