JCSE, vol. 10, no. 2, pp.58-67, June, 2016

DOI: http://dx.doi.org/10.5626/JCSE.2016.10.2.58

Truncated Kernel Projection Machine for Link Prediction

Liang Huang, Ruixuan Li, and Hong Chen
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China College of Science, Huazhong Agricultural University, Wuhan, China

Abstract: With the large amount of complex network data that is increasingly available on the Web, link prediction has become a popular data-mining research field. The focus of this paper is on a link-prediction task that can be formulated as a binary classification problem in complex networks. To solve this link-prediction problem, a sparse-classification algorithm called ?쏷runcated Kernel Projection Machine??that is based on empirical-feature selection is proposed. The proposed algorithm is a novel way to achieve a realization of sparse empirical-feature-based learning that is different from those of the regularized kernel-projection machines. The algorithm is more appealing than those of the previous outstanding learning machines since it can be computed efficiently, and it is also implemented easily and stably during the link-prediction task. The algorithm is applied here for link-prediction tasks in different complex networks, and an investigation of several classification algorithms was performed for comparison. The experimental results show that the proposed algorithm outperformed the compared algorithms in several key indices with a smaller number of test errors and greater stability.

Keyword: TKPM; Link prediction; Empirical feature; Reproducing-kernel Hilbert space; Mercer kernel

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