Self-organizing maps
Dimension reduction by local principal component analysis
Neural Computation
Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
Mixtures of probabilistic principal component analyzers
Neural Computation
Visualizing Data
Information Visualization in Data Mining and Knowledge Discovery
Information Visualization in Data Mining and Knowledge Discovery
Designing Pixel-Oriented Visualization Techniques: Theory and Applications
IEEE Transactions on Visualization and Computer Graphics
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
30 Years of Multidimensional Multivariate Visualization
Scientific Visualization, Overviews, Methodologies, and Techniques
Visual hierarchical dimension reduction for exploration of high dimensional datasets
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
Recursive Pattern: A Technique for Visualizing Very Large Amounts of Data
VIS '95 Proceedings of the 6th conference on Visualization '95
Clustered principal components for precomputed radiance transfer
ACM SIGGRAPH 2003 Papers
Inventing discovery tools: combining information visualization with data mining
Information Visualization
Shape coding of multidimensional data on a microcomputer display
VIS '90 Proceedings of the 1st conference on Visualization '90
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
XmdvTool: integrating multiple methods for visualizing multivariate data
VIS '94 Proceedings of the conference on Visualization '94
APVis '06 Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation - Volume 60
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We propose to integrate information visualization techniques with factor analysis. Specifically, a principal direction derived from a principal component analysis (PCA) of the data is displayed together with the data in a scatterplot matrix. The direction can be adjusted to coincide with visual trends in the data. Projecting the data onto the orthogonal subspace allows determining the next direction. The set of directions identified in this way forms an orthogonal space, which represents most of the variation in the data. We call this process visual component analysis (VCA). Furthermore, it is quite simple to integrate VCA with clustering. The user fits poly-lines to the displayed data, and the poly-lines implicitly define clusters. Per-cluster projection leads to the definition of per-cluster components.