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Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations according to the Hilbert Schmidt independence criterion (HSIC). It is unique in that structure information on the cluster outputs can be easily utilized in the clustering process. However, while the choice of the loss function...
In this paper, in order to reduce the support vectors on a large scale data set, we train support vector machines which utilize the hyper-spheres as the training samples. By representing adjacent samples of the same class as hyper-spheres, the boundary location can be controlled both by the center and radius of the hyper-spheres. We demonstrate that the optimization problem in this condition can be...
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