Physical Sciences and Mathematics Commons^™

Deep Learning Of 2-D Images Representing N-D Data In General Line Coordinates, Dmytro Dovhalets, Boris Kovalerchuk, Szilárd Vajda, Răzvan Andonie

Computer Science Faculty Scholarship

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 …