Neural Generalization of Multiple Kernel Learning

Abstract

Multiple Kernel Learning (MKL) is a conventional way to learn the kernel function in kernel-based methods. MKL algorithms enhance the performance of kernel methods. However, these methods have a lower complexity compared to deep models and are inferior to them regarding recognition accuracy. Deep learning models can learn complex functions by applying nonlinear transformations to data through several layers. In this paper, we show that a typical MKL algorithm can be interpreted as a one-layer neural network with linear activation functions. By this interpretation, we propose a Neural Generalization of Multiple Kernel Learning (NGMKL), which extends the conventional MKL framework to a multi-layer neural network with nonlinear activation functions. Our experiments show that the proposed method, which has a higher complexity than traditional MKL methods, leads to higher recognition accuracy on several benchmarks.