Systematic Methods for Multi Variate Data Visualization and Numerical Asessment of Class Separability and Overlap in Automated Visual Industrial Quality Control

The focus of this work is on systematic methods for visualization and quality assessment with regards to classification of multivariate data sets. This novel methods and criteria give in visual and numerical form rapid insight in the principal data distribution, the degree of compactness and overlap of class regions and class separability, as well as information to identify outliers in the data set and trace them back to data acquisition. Assessment by visualization and numerical criteria can be exploited for interactive or automatic optimization of feature generation and selection/extraction in pattern recognition problems.