Kernel-based clustering is supposed to provide a better analysis tool for pattern classification,which implicitly maps input samples to a highdimensional space for improving pattern separability.For this implicit spac...Kernel-based clustering is supposed to provide a better analysis tool for pattern classification,which implicitly maps input samples to a highdimensional space for improving pattern separability.For this implicit space map,the kernel trick is believed to elegantly tackle the problem of“curse of dimensionality”,which has actually been more challenging for kernel-based clustering in terms of computational complexity and classification accuracy,which traditional kernelized algorithms cannot effectively deal with.In this paper,we propose a novel kernel clustering algorithm,called KFCM-III,for this problem by replacing the traditional isotropic Gaussian kernel with the anisotropic kernel formulated by Mahalanobis distance.Moreover,a reduced-set represented kernelized center has been employed for reducing the computational complexity of KFCM-I algorithm and circumventing the model deficiency of KFCM-II algorithm.The proposed KFCMIII has been evaluated for segmenting magnetic resonance imaging(MRI)images.For this task,an image intensity inhomogeneity correction is employed during image segmentation process.With a scheme called preclassification,the proposed intensity correction scheme could further speed up image segmentation.The experimental results on public image data show the superiorities of KFCM-III.展开更多
基金This work was partially supported by the National Natural Science Foundation of China(Grant Nos.60872145,60902063)the National High Technology Research and Development Program of China(Grant No.2009AA01Z315)+1 种基金the Cultivation Fund of the Key Scientific and Technical Innovation Project,Ministry of Education of China(No.708085)the Henan Research Program of Foundation and Advanced Technology(No.082300410090).
文摘Kernel-based clustering is supposed to provide a better analysis tool for pattern classification,which implicitly maps input samples to a highdimensional space for improving pattern separability.For this implicit space map,the kernel trick is believed to elegantly tackle the problem of“curse of dimensionality”,which has actually been more challenging for kernel-based clustering in terms of computational complexity and classification accuracy,which traditional kernelized algorithms cannot effectively deal with.In this paper,we propose a novel kernel clustering algorithm,called KFCM-III,for this problem by replacing the traditional isotropic Gaussian kernel with the anisotropic kernel formulated by Mahalanobis distance.Moreover,a reduced-set represented kernelized center has been employed for reducing the computational complexity of KFCM-I algorithm and circumventing the model deficiency of KFCM-II algorithm.The proposed KFCMIII has been evaluated for segmenting magnetic resonance imaging(MRI)images.For this task,an image intensity inhomogeneity correction is employed during image segmentation process.With a scheme called preclassification,the proposed intensity correction scheme could further speed up image segmentation.The experimental results on public image data show the superiorities of KFCM-III.
