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Localized multiple kernel learning is a promising strategy for combining multiple features or kernels in terms of their discriminative power. However, learning specific combination kernel for each sample generally leads to expensive computation and less reliability caused by some dominant samples. In addition, traditional sparse constraint generally causes that one of the best kernels is selected, while some useful kernels may have not been used efficiently. In this paper, we proposed adoi:10.24507/ijicic.12.06.1835 fatcat:3fkad4wyqzddposs33tej2lmna