Gaussian bandwidth selection for manifold learning and classification

DOI10.1007/s10618-020-00692-xzbMath1455.68174arXiv1707.01093OpenAlexW3039300151WikidataQ98661528 ScholiaQ98661528MaRDI QIDQ2212526

Arie Yeredor, Moshe Salhov, Ofir Lindenbaum, Amir Z. Averbuch

Publication date: 23 November 2020

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1707.01093

zbMATH Keywords

classificationkernel methodsdimensionality reductiondiffusion maps

Mathematics Subject Classification ID

Statistics on manifolds (62R30) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (9)

IAN: Iterated Adaptive Neighborhoods for Manifold Learning and Dimensionality Estimation ⋮ eGHWT: the extended generalized Haar-Walsh transform ⋮ Natural graph wavelet packet dictionaries ⋮ Geometric scattering on measure spaces ⋮ Spatiotemporal analysis using Riemannian composition of diffusion operators ⋮ Kernel embedding based variational approach for low-dimensional approximation of dynamical systems ⋮ Kernel-based parameter estimation of dynamical systems with unknown observation functions ⋮ Spectral Convergence of Diffusion Maps: Improved Error Bounds and an Alternative Normalization ⋮ A mixture model approach to spectral clustering and application to textual data

Uses Software

• UCI-ml
• IntDim
• COVID-Net

Cites Work

• Unnamed Item
• Data dimensionality estimation methods: A survey.
• Learning the geometry of common latent variables using alternating-diffusion
• Principal component analysis.
• Multi-view kernel consensus for data analysis
• Diffusion maps
• DANCo: an intrinsic dimensionality estimator exploiting angle and norm concentration
• An Intrinsic Dimensionality Estimator from Near-Neighbor Information
• Stastical Estimation of the Intrinsic Dimensionality of a Noisy Signal Collection
• Graph Laplacian Tomography From Unknown Random Projections
• On the Volume Elements on a Manifold
• An Algorithm for Finding Intrinsic Dimensionality of Data
• Choosing multiple parameters for support vector machines

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