Generalization Bounds for Coregularized Multiple Kernel Learning

Multiple kernel learning (MKL) as an approach to automated kernel selection plays an important role in machine learning. Some learning theories have been built to analyze the generalization of multiple kernel learning. However, less work has been studied on multiple kernel learning in the framework of semisupervised learning. In this paper, we analyze the generalization of multiple kernel learning in the framework of semisupervised multiview learning. We apply Rademacher chaos complexity to control the performance of the candidate class of coregularized multiple kernels and obtain the generalization error bound of coregularized multiple kernel learning. Furthermore, we show that the existing results about multiple kennel learning and coregularized kernel learning can be regarded as the special cases of our main results in this paper.