A spectrum-based support vector algorithm for relational data semi-supervised classification

Authors:
Ling Ping;Wang Zhe;Zhou Chunguang
Affiliations:
College of Computer Science, Jilin University, Key Laboratory of Symbol Computation, and Knowledge Engineering of the Ministry of Education, Changchun, China;College of Computer Science, Jilin University, Key Laboratory of Symbol Computation, and Knowledge Engineering of the Ministry of Education, Changchun, China;College of Computer Science, Jilin University, Key Laboratory of Symbol Computation, and Knowledge Engineering of the Ministry of Education, Changchun, China
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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Abstract

A Spectrum-based Support Vector Algorithm (SSVA) to resolve semi-supervised classification for relational data is presented in this paper. SSVA extracts data representatives and groups them with spectral analysis. Label assignment is done according to affinities between data and data representatives. The Kernel function encoded in SSVA is defined to rear to relational version and parameterized by supervisory information. Another point is the self-tuning of penalty coefficient and Kernel scale parameter to eliminate the need of searching parameter spaces. Experiments on real datasets demonstrate the performance and efficiency of SSVA.