Semi-supervised classification with Laplacian multiple kernel learning

Abstract

Laplacian Support Vector Machine (lapSVM) is a SVM with an additional graph-based regularization for semi-supervised learning (SSL). As its base classifier is a single kernel SVM, it may be inefficient to deal with multi-source or multi-attribute complex datasets. Instead of one single kernel, we know that multiple kernels could correspond to different notions of similarity or information from multiple sources and represent differences between features. Therefore, we extend lapSVM to multiple kernel occasion, namely Laplacian Multiple Kernel Learning (lapMKL), improving the ability of processing more complex data in semi-supervised classification task. The proposed lapMKL is solved by Level Method, which was used in multiple kernel learning (MKL) and showed relatively high efficiency. Experiments on several data sets and comparisons with state of the art methods show that the proposed lapMKL is competitive and even better.