In this paper, the 3D head model classification problem is addressed by use of a newly developed subspace analysis method: kernel clustering-based discriminant analysis or KCDA as an abbreviation. This method works by first mapping the original data into another high-dimensional space, and then performing clustering-based discriminant analysis in the feature space. The main idea of clustering-based discriminant analysis is to overcome the Gaussian assumption limitation of the traditional linear discriminant analysis by using a new criterion that takes into account the multiple cluster structure possibly embedded within some classes. As a result, Kernel CDA tries to get through the limitations of both Gaussian assumption and linearity facing the traditional linear discriminant analysis simultaneously. A novel application of this method in 3D head model classification is presented in this paper. A group of tests of our method on 3D head model dataset have been carried out, reporting very promising experimental results. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Ma, B., Qu, H. Y., Wong, H. S., & Lu, Y. (2006). 3D head model classification using KCDA. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4261 LNCS, pp. 1008–1017). Springer Verlag. https://doi.org/10.1007/11922162_114
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