Deep kernel learning in extreme learning machines

Emergence of extreme learning machine as a breakneck learning algorithm has marked its prominence in solitary hidden layer feed-forward networks. Kernel-based extreme learning machine (KELM) reflected its efficiency in diverse applications where feature mapping functions of hidden nodes are concealed from users. The conventional KELM algorithms involve only solitary layer of kernels, thereby emulating shallow learning architectures for its feature transformation. Trend in migrating shallow-based learning models into deep learning architectures opens up a new outlook for machine learning domains. This paper attempts to bestow deep kernel learning approach in a conventional shallow architecture. The emerging arc-cosine kernels possess the potential to mimic the prevailing deep layered frameworks to a greater extent. Unlike other kernels such as linear, polynomial and Gaussian, arc-cosine kernels have a recursive nature by itself and have the potential to express multilayer computation in learning models. This paper explores the possibility of building a new deep kernel machine with extreme learning machine and multilayer arc-cosine kernels. This framework outperforms conventional KELM and deep support vector machine in terms of training time and accuracy.