OPTIMIZED DICTIONARY DESIGN AND CLASSIFICATION USING THE MATCHING PURSUITS DISSIMILARITY MEASURE By RAAZIA MAZHAR A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy OPTIMIZED DICTIONARY DESIGN AND CLASSIFICATION USING THE MATCHING PURSUITS DISSIMILARITY MEASURE By Raazia Mazhar May 2009 Chair: Paul D. Gader Co-chair: Joseph N. Wilson Major: Computer Engineering Discrimination-based classifiers differentiate between two classes by drawing a decision boundary between their data members in the feature domain. These classifiers are capable of correctly labeling the test data that belongs to the same distribution as the training data. However, since the decision boundary is meaningless beyond the training points, the class label of an outlier determined with respect to this extended decision boundary will be a random value. Therefore, discrimination-based classifiers lack a mechanism for outlier detection in the test data. To counter this problem, a prototype-based classifier may be used that assigns class label to a test point based on its similarity to the prototype of that class. If a test point is dissimilar to all class prototypes, it may be considered an outlier. Prototype-based classifiers are usually clustering-based methods. Therefore, they require a dissimilarity criterion to cluster the training data and also to assign class labels to test data. Euclidean distance is a commonly used dissimilarity criterion. However, the Euclidean distance may not be able to give accurate shape-based comparisons of very high-dimensional signals. This can be problematic for some classification applications where high-dimensional signals are grouped into classes based on shape similarities. Therefore, a reliable shape-based dissimilarity measure is desirable. Inorder to be able to build reliable prototype-based classifiers that can utilize shapebased information for classification, we have developed a matching pursuits dissimilarity