Journal
PATTERN ANALYSIS AND APPLICATIONS
Volume 24, Issue 1, Pages 11-19Publisher
SPRINGER
DOI: 10.1007/s10044-020-00891-8
Keywords
Extreme learning machines; Deep kernel machines; Arc-cosine kernel; Deep kernel extreme learning machines
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The emergence of extreme learning machine as a rapid learning algorithm has marked its prominence in solitary hidden layer feed-forward networks. This paper introduces a deep kernel learning approach in a conventional shallow architecture and explores the possibility of building a new deep kernel machine with extreme learning machine and multilayer arc-cosine kernels.
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.
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