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On Optimizing Dissimilarity-Based Classifier Using Multi-level Fusion Strategies  

Kim, Sang-Woon (Dept. of Computer Engineering, Myongji University)
Duin, Robert P. W. (Delft University of Technology, The Netherlands)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.45, no.5, 2008 , pp. 15-24 More about this Journal
Abstract
For high-dimensional classification tasks, such as face recognition, the number of samples is smaller than the dimensionality of the samples. In such cases, a problem encountered in linear discriminant analysis-based methods for dimension reduction is what is known as the small sample size (SSS) problem. Recently, to solve the SSS problem, a way of employing a dissimilarity-based classification(DBC) has been investigated. In DBC, an object is represented based on the dissimilarity measures among representatives extracted from training samples instead of the feature vector itself. In this paper, we propose a new method of optimizing DBCs using multi-level fusion strategies(MFS), in which fusion strategies are employed to represent features as well as to design classifiers. Our experimental results for benchmark face databases demonstrate that the proposed scheme achieves further improved classification accuracies.
Keywords
Dissimilarity-Based Classification(DBC); Multilevel Fusion Strategy(MFS); Small Sample Size Problem;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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