Matching-pursuits dissimilarity measure for shape-based comparison and classification of high-dimensional data

  • Authors:

  • Raazia Mazhar;Paul D. Gader;Joseph N. Wilson

  • Affiliations:

  • Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL;Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL;Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL

  • Venue:
  • IEEE Transactions on Fuzzy Systems
  • Year:
  • 2009

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Abstract

In this paper, a new matching-pursuits dissimilarity measure (MPDM) is presented that compares two signals using the information provided by their matching pursuits (MP) approximations, without requiring any prior domain knowledge. MPDM is a flexible and differentiable measure that can be used to perform shape-based comparisons and fuzzy clustering of very high-dimensional, possibly compressed, data. A novel prototype-based classification algorithm, which is termed the computer-aided minimization procedure (CAMP), is also proposed. The CAMP algorithm uses theMPDM with the competitive agglomeration (CA) fuzzy clustering algorithm to build reliable shape-based prototypes for classification. MP is a well-known sparse-signal approximation technique, which is commonly used for video and image coding. The dictionary and coefficient information produced by MP has previously been used to define features to build discrimination- and prototype-based classifiers. However, existing MP-based classification applications are quite problem-domain specific, thus making their generalization to other problems quite difficult. The proposed CAMP algorithm is the first MP-based classification system that requires no assumptions about the problem domain and builds a bridge between the MP and fuzzy clustering algorithms. Experimental results also show that the CAMP algorithm is more resilient to outliers in test data than the multilayer perceptron (MLP) and support-vector-machine (SVM) classifiers, as well as prototype-based classifiers using the Euclidean distance as their dissimilarity measure.