  Multiple kernel learning is a type of multiview learning that combines
different data modalities by capturing view-specific patterns using kernels.
Although supervised multiple kernel learning has been extensively studied,
until recently, only a few unsupervised approaches have been proposed. In the
meanwhile, adversarial learning has recently received much attention. Many
works have been proposed to defend against adversarial examples. However,
little is known about the effect of adversarial perturbation in the context of
multiview learning, and even less in the unsupervised case. In this study, we
show that adversarial features added to a view can make the existing approaches
with the min-max formulation in multiple kernel clustering yield unfavorable
clusters. To address this problem and inspired by recent works in adversarial
learning, we propose a multiple kernel clustering method with the min-max
framework that aims to be robust to such adversarial perturbation. We evaluate
the robustness of our method on simulation data under different types of
adversarial perturbations and show that it outperforms several compared
existing methods. In the real data analysis, We demonstrate the utility of our
method on a real-world problem.
