A simple feature combination method based on dominant sets
@article{Hou2013ASF, title={A simple feature combination method based on dominant sets}, author={Jian Hou and Marcello Pelillo}, journal={Pattern Recognition}, year={2013}, volume={46}, pages={3129-3139} }
- Published 2013 in Pattern Recognition
DOI:10.1016/j.patcog.2013.04.005
Feature combination is a popular method for improving object classification performances. In this paper we present a simple and effective weighting scheme for feature combination based on the dominant-set notion of a cluster. Specifically, we use dominant sets clustering to evaluate how accurate a kernel matrix is expected to be for a SVM classifier. This expected kernel accuracy reflects the discriminative power of the kernel matrix and thus used in weighting the kernel matrix in feature… CONTINUE READING
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References
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