Semi‐supervised multiple empirical kernel learning with pseudo empirical loss and similarity regularization

Multiple empirical kernel learning (MEKL) is a scalable and efficient supervised algorithm based on labeled samples. However, there is still a huge amount of unlabeled samples in the real‐world application, which are not applicable for the supervised algorithm. To fully utilize the spatial distribut...

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