Visual Pattern Recognition Method Based on Optimized Parallel Coordinates

A visual pattern recognition method based on optimized parallel coordinates is proposed in this paper. We first introduce the traditional theory of parallel coordinates and indicate that parallel coordinates has a potential for classification tasks due to its projective transformation interpretation. Nevertheless, some optimization is needed. The main aim of optimization is to hide the valueless information and reveal the most valuable information for classification. Three interaction operations are proposed to do this work. We demonstrate by several examples how to solve some pattern recognition problems graphically, we also point out that some start-of-the-art classification techniques such as Fisher linear discriminant analysis, decision trees and support vector machines are related to and can be visualized by this method. By cooperation and union of this method with some machine learning and human-machine interaction techniques into a system, the black boxes of some pattern recognition techniques are expected to be opened up, the original data, the process of classification and final results will be more transparency and understandable.