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Then the correlation clustering objective asks to minimize the sum of (the weights of) positive edges across clusters plus the sum of (the weights) of negative edges within clusters.

Moreover,the proposed multi-class kernel learning algorithms have statistical guarantees and fast convergence rates. Experimental results on lots of benchmark datasets show that our proposed methods can significantly outperform the existing multi-class classification methods. The major contributions ofthispaper include: 1)Anewlocal Rademacher complexitybased bound withfastconvergence rate for multi-class classification is established. Existing works [16,27] for multi-class classifiers with Rademacher complexity does not take into account couplings among different classes.

It has been well documented that high dimensional data approximately resides on low dimensional manifolds.

More real-world downstream tasks are displayed in Figure 4 and Figure 5.

Traditionally, this transferability is always regarded as a critical threat to the defense against adversarial attacks, however, we argue that the network robustness can be significantly boosted by utilizing adversarial transferability from anewperspective.