Simulated annealing: theory and applications
Simulated annealing: theory and applications
Graph drawing by force-directed placement
Software—Practice & Experience
Fast multidimensional scaling through sampling, springs and interpolation
Information Visualization
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Self-organizing map learning nonlinearly embedded manifolds
Information Visualization
Visualization of a closed three-dimensional surface using portal-based rendering
APVis '06 Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation - Volume 60
RankVisu: Mapping from the neighborhood network
Neurocomputing
Large-scale multidimensional data visualization: a web service for data mining
ServiceWave'11 Proceedings of the 4th European conference on Towards a service-based internet
Computer Methods and Programs in Biomedicine
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ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Improvement of data visualization based on ISOMAP
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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This paper introduces a method called relational perspective map (RPM) to visualize distance information in high-dimensional spaces. Like conventional multidimensional scaling, the RPM algorithm aims to produce proximity preserving 2-dimensional (2-D) maps. The main idea of the RPM algorithm is to simulate a multiparticle system on a closed surface: whereas the repulsive forces between the particles reflect the distance information, the closed surface holds the whole system in balance and prevents the resulting map from degeneracy. A special feature of RPM algorithm is its ability to partition a complex dataset into pieces and map them onto a 2-D space without overlapping. Compared to other multidimensional scaling methods, RPM is able to reveal more local details of complex datasets. This paper demonstrates the properties of RPM maps with four examples and provides extensive comparison to other multidimensional scaling methods, such as Sammon Mapping and Curvilinear Principle Analysis.