Constructing Interactive Visual Classification, Clustering and Dimension Reduction Models for n-D Data
Journal Title: Informatics - Year 2017, Vol 4, Issue 3
Abstract
The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the loss of information, occlusion, and clutter continue to be a challenge. This paper proposes and explores a new interactive method for visual discovery of n-D relations for supervised learning. The method includes automatic, interactive, and combined algorithms for discovering linear relations, dimension reduction, and generalization for non-linear relations. This method is a special category of reversible General Line Coordinates (GLC). It produces graphs in 2-D that represent n-D points losslessly, i.e., allowing the restoration of n-D data from the graphs. The projections of graphs are used for classification. The method is illustrated by solving machine-learning classification and dimension-reduction tasks from the domains of image processing, computer-aided medical diagnostics, and finance. Experiments conducted on several datasets show that this visual interactive method can compete in accuracy with analytical machine learning algorithms.
Authors and Affiliations
Boris Kovalerchuk and Dmytro Dovhalets
Keywords
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- EP ID EP44094
- DOI https://doi.org/10.3390/informatics4030023
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