Visual Pattern Recognition Based On Subspace Coordinates Graphical Representation Of Multivariate Data

Pattern recognition is one of the basic intelligence of human and other senior animals. Human have excellent pattern recognition capability in most cases, and this capability is considered as a nature. However, teaching machine to deal with the same pattern recognition problem is not so easy. After a long research time of several decades, up to this day the mechanism of human pattern recognition is not well grasped. Although the theories and methods of automatic pattern recognition by computers have been fully studied and great successes have been made, there are some well known open problems such as small samples problem, dimension curse, black-box problem and so on. Fully automation is still one of the design criteria of pattern recognition system, the interactions of human and machine are reduced to the least. Although in the stage of designing classifier, some techniques of exploratory data analysis and visualization are used occasionally, these methods are not combined with the pattern recognition algorithms compactly. Usually only the original data or classification results are visualized.As an important way of data analysis, multivariate data visualization techniques have been applied in many domains. Up to now, the relationship of these multivariate data visualization techniques has not been fully researched. A united theoretical basis of various graphical representation methods is still not found. In order to realize visual pattern recognition by integration of multivariate graphical representation methods and machine algorithms, there are some basic problems to be solved. Work of this thesis focuses on three basic problems: How to construct a describing model of several popular multivariate graphical representation methods? How to optimize these multivariate graphical representation methods for pattern recognition application? How to integrate the machine algorithms and multivariate graphical representation methods for visual classification?Firstly, the representation principles and characteristics of several popular multivariate graphical representation methods are investigated. And then a general graphical representation model of multivariate data subspace coordinates is presented. This model united the scatter plot, scatter plot matrix, nomogram, parallel coordinates, Andrews'plot and star glyph to the same representation framework, so as to facilitate not only researches on the differences and relationships of these methods but also the development of new graphical representation methods.Secondly, 2D dual coordinates is defined,the representation characteristics are studied and several theorems are proved. Consequently a new multivariate visualization method named multivariate parallel dual plot is developed. This method integrates multiple scatter plots with the parallel coordinates, moreover the dual coordinates representation and parallel coordinates representation of the same sample has determined geometrical relationship. The two representation forms can be switched according to actual needing, consequently combing the merits of both methods and overcome their shortcomings. The three dimensional display of 2D dual coordinates and 3D dual coordinates representation are also investigated and representation examples are provided.Lastly, the problem of graphical features optimization is studied. The optimization of parallel coordinates by convex hull, the weights optimization of constellation graph by complex linear discriminant analysis and the rapid optimization of Radviz are proposed. Some machine learning algorithms are combined with parallel coordinates, and three visual classifiers based on the optimization of parallel coordinates are proposed: the visual BP neural network, the parallel filter visual classifier and the Bayes visual classifier. Some experiments are done using data sets such as vegetable oil classification, fault diagnostics and disease diagnostics.This research indicate these visual pattern recognition methods have the merits of pattern visualization (making the invisible visible), making the representation of complex system simple and facilitating the utilizing and generating of expert knowledge. It is expected to develop this method further and apply it to some domains'complex pattern recognition problems.