Nonlinear dimension reduction for functional data with application to clustering
From MaRDI portal
Publication:6593368
DOI10.5705/SS.202021.0393MaRDI QIDQ6593368
Guosheng Yin, Yiming Zang, [[Person:6091916|Author name not available (Why is that?)]]
Publication date: 26 August 2024
Published in: STATISTICA SINICA (Search for Journal in Brave)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Nonlinear manifold representations for functional data
- Smooth principal component analysis over two-dimensional manifolds with an application to neuroimaging
- Statistical tools to analyze data representing a sample of curves
- Principal component analysis for functional data on Riemannian manifolds and spheres
- Intrinsic Riemannian functional data analysis
- Functional data analysis.
- Vector diffusion maps and the connection Laplacian
- An Effective Bandwidth Selector for Local Least Squares Regression
- Time ordering of gene coexpression
- Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Clustering Functional Data into Groups by Using Projections
- Parallel Transport Unfolding: A Connection-Based Manifold Learning Approach
- Functional Data Analysis for Sparse Longitudinal Data
- Functional regression on the manifold with contamination
- Modeling sparse longitudinal data on Riemannian manifolds
This page was built for publication: Nonlinear dimension reduction for functional data with application to clustering
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6593368)
