A General Framework for Dimensionality-Reducing Data Visualization Mapping
From MaRDI portal
Publication:2885113
DOI10.1162/NECO_a_00250zbMath1238.68117WikidataQ60191362 ScholiaQ60191362MaRDI QIDQ2885113
Kerstin Bunte, Barbara Hammer, Michael Biehl
Publication date: 21 May 2012
Published in: Neural Computation (Search for Journal in Brave)
Related Items (11)
Parametric UMAP Embeddings for Representation and Semisupervised Learning ⋮ Automatic topography of high-dimensional data sets by non-parametric density peak clustering ⋮ A graph-based N-body approximation with application to stochastic neighbor embedding ⋮ Object embedding using an information geometrical perspective ⋮ Supervised learning of explicit maps with ability to correct distortions in the target output for manifold learning ⋮ Indefinite Proximity Learning: A Review ⋮ Neural Decoding with Kernel-Based Metric Learning ⋮ On the use of \(t\)-distributed stochastic neighbor embedding for data visualization and classification of individuals with Parkinson's disease ⋮ Dimensionality reduction: an interpretation from manifold regularization perspective ⋮ Dimensionality reduction by supervised neighbor embedding using Laplacian search ⋮ A comparative study: globality versus locality for graph construction in discriminant analysis
Uses Software
Cites Work
- Unnamed Item
- Kernel clustering-based discriminant analysis
- Improved learning of Riemannian metrics for exploratory analysis
- Batch and median neural gas
- Multidimensional scaling. I: Theory and method
- Adaptive Relevance Matrices in Learning Vector Quantization
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- 10.1162/153244303321897690
- 10.1162/153244303322753616
- Semidefinite Programming
- Nonlinear Dimensionality Reduction
- Parametric Embedding for Class Visualization
This page was built for publication: A General Framework for Dimensionality-Reducing Data Visualization Mapping
