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

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 selftuning 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. © Springer-Verlag Berlin Heidelberg 2006.