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A similarity or dissimilarity measure, such as the Euclidean distance, is crucial to discriminative clustering algorithms. These measures used to calculate pairwise similarities between samples rely on data representations in a feature space. However, discriminative clustering fails if the samples in a feature space are linearly inseparable. This problem can be solved by performing a nonlinear data...
Two-stage multiple kernel learning (MKL) algorithms have been extensively researched in recent years due to their high efficiency and effectiveness. Previous works have attempted to optimize the combination coefficients by maximizing the centralized kernel alignment between the combined kernel and the ideal kernel. Though demonstrating previous promising performance, we observe that these algorithms...
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