Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
A Hierarchical Latent Variable Model for Data Visualization
IEEE Transactions on Pattern Analysis and Machine Intelligence
GTM: the generative topographic mapping
Neural Computation
Learning in graphical models
ACM Computing Surveys (CSUR)
Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Robust mixture modelling using the t distribution
Statistics and Computing
An introduction to variable and feature selection
The Journal of Machine Learning Research
Magnification Control in Self-Organizing Maps and Neural Gas
Neural Computation
New indices for cluster validity assessment
Pattern Recognition Letters
VizRank: Data Visualization Guided by Machine Learning
Data Mining and Knowledge Discovery
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Magnification control for batch neural gas
Neurocomputing
Neural Networks
Semi-supervised geodesic Generative Topographic Mapping
Pattern Recognition Letters
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Application notes: data mining in cancer research
IEEE Computational Intelligence Magazine
Vision: Images, Signals and Neural Networks Models of Neural Processing in Visual Perception
Vision: Images, Signals and Neural Networks Models of Neural Processing in Visual Perception
GPU-Assisted Computation of Centroidal Voronoi Tessellation
IEEE Transactions on Visualization and Computer Graphics
Relational generative topographic mapping
Neurocomputing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Preconceptions and individual differences in understanding visual metaphors
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Piecewise laplacian-based projection for interactive data exploration and organization
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Hi-index | 0.00 |
Real-world applications of multivariate data analysis often stumble upon the barrier of interpretability. Simple data analysis methods are usually easy to interpret, but they risk providing poor data models. More involved methods may instead yield faithful data models, but limited interpretability. This is the case of linear and nonlinear methods for multivariate data visualization through dimensionality reduction. Even though the latter have provided some of the most exciting visualization developments, their practicality is hindered by the difficulty of explaining them in an intuitive manner. The interpretability, and therefore the practical applicability, of data visualization through nonlinear dimensionality reduction (NLDR) methods would improve if, first, we could accurately calculate the distortion introduced by these methods in the visual representation and, second, if we could faithfully reintroduce this distortion into such representation. In this paper, we describe a technique for the reintroduction of the distortion into the visualization space of NLDR models. It is based on the concept of density-equalizing maps, or cartograms, recently developed for the representation of geographic information. We illustrate it using Generative Topographic Mapping (GTM), a nonlinear manifold learning method that can provide both multivariate data visualization and a measure of the local distortion that the model generates. Although illustrated here with GTM, it could easily be extended to other NLDR visualization methods, provided a local distortion measure could be calculated. It could also serve as a guiding tool for interactive data visualization.