Deep kernel learning in core vector machines

• Published in: Pattern analysis and applications : PAA Vol. 21; no. 3; pp. 721 - 729
• Main Authors: Afzal, A. L., Asharaf, S.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2018; Springer Nature B.V
• Subjects: Artificial intelligence; Artificial neural networks; Classification; Computer Science; Datasets; Machine learning; Neural networks; Pattern Recognition; Quadratic programming; Scaling up; Support vector machines; Theoretical Advances; Training; Support vector machines; Arc-cosine kernel; Core vector machines; Scalability; Deep kernel
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-017-0600-4

In machine learning literature, deep learning methods have been moving toward greater heights by giving due importance in both data representation and classification methods. The recently developed multilayered arc-cosine kernel leverages the possibilities of extending deep learning features into the kernel machines. Even though this kernel has been widely used in conjunction with support vector machines (SVM) on small-size datasets, it does not seem to be a feasible solution for the modern real-world applications that involve very large size datasets. There are lot of avenues where the scalability aspects of deep kernel machines in handling large dataset need to be evaluated. In machine learning literature, core vector machine (CVM) is being used as a scaling up mechanism for traditional SVMs. In CVM, the quadratic programming problem involved in SVM is reformulated as an equivalent minimum enclosing ball problem and then solved by using a subset of training sample (Core Set) obtained by a faster ( 1 + ϵ ) approximation algorithm. This paper explores the possibilities of using principles of core vector machines as a scaling up mechanism for deep support vector machine with arc-cosine kernel. Experiments on different datasets show that the proposed system gives high classification accuracy with reasonable training time compared to traditional core vector machines, deep support vector machines with arc-cosine kernel and deep convolutional neural network.