Kernel conditional random fields : representation, clique selection, and semi-supervised learning
Kernel conditional random fields are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs. A procedure for greedily selecting cliques in the dual representation is then proposed, which allows sparse representations. By incorporating kernels and implicit feature spaces into conditional graphical models, the framework enables semi-supervised learning algorithms for structured data through the use of graph kernels. The clique selection and semisupervised methods are demonstrated in synthetic data experiments, and are also applied to the problem of protein secondary structure prediction. This research was partially supported by the National Science Foundation (NSF), under grants IIS-0205456 and IIS-0312814. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation, or the U.S. government.