A review of approximate methods for kernel-based big media data analysis

With the increasing size of today's image and video data sets, standard pattern recognition approaches, like kernel based learning, need to face new challenges. Kernel-based methods require the storage and manipulation of the kernel matrix, having dimensions equal to the number of training samples. When the data set cardinality becomes large, the application of kernel methods becomes intractable. Approximate kernel-based learning approaches have been proposed in order to reduce the time and space complexities of kernel methods, while achieving satisfactory performance. In this paper, we provide a overview of such approximate kernel-based learning approaches finding application in media data analysis.