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In this study, two clustering frameworks are proposed based on a maximizing model of spherical Bezdek-type fuzzy clustering are proposed. One using possibilistic c-means, and the other using multi-medoids. In each framework, the basic model and its kernelization are presented, along with an appropriate spectral clustering technique. Kernelization allows the frameworks to capture nonlinear-bordered...
In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured...
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