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While knowledge discovery and n-D data visualization procedures are often efficient, the loss of information, occlusion, and clutter continue to be a challenge. General Line Coordinates (GLC) is a rather new technique to deal with such artifacts. GLC-Linear, which is one of the methods in GLC, allows transforming n-D numerical data to their visual representation as polylines losslessly. The method proposed in this paper uses these 2-D visual representations as input to Convolutional Neural Network (CNN) classifiers. The obtained classification accuracies are close to the ones obtained by other machine learning algorithms. The main benefit of the method is the possibility to use the lossless visualization of n-dimensional data for interpretation and explanation of the discovered relationships besides the classical classification using statistical learning strategies.
Dovhalets, Dmytro; Kovalerchuk, Boris; Vajda, Szilárd; and Andonie, Răzvan, "Deep Learning of 2-D Images Representing n-D Data in General Line Coordinates" (2018). Computer Science Faculty Scholarship. 1.
conference ISASE-MAICS 2018
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