A review of approximate methods for kernel-based big media data analysis
Large-scale multi-class classification problems involve an enormous amount of training data that make the application of classical non-linear classification algorithms difficult. In addition, such multi-class classification problems are usually formed by a considerable number of classes. This makes the application of the popular one-versus-rest binary classifiers fusion scheme adopted by most state-of-the-art approaches difficult. In this paper, in order to overcome the high computational cost of multi-class non-linear classification approaches, we adopt an ensemble of approximate non-linear one-class classifiers. To this end, we propose a new scalable solution for the Least Squares One-Class Support Vector Machine classifier by following an approximate kernel approach. We evaluated the proposed method in big data visual classification problems, where it is shown that it is able to achieve satisfactory performance, while significantly reducing the overall computational and memory costs.