Kernel Learning for Novelty Detection

We consider kernel learning for one-class Support Vector Machines. We consider a mix of 2- and 1-norms of the individual weight vector norms allowing control of the sparsity of the resulting kernel combination. The resulting optimisation can be solved efficiently using a coordinate gradient method. We consider an application to automatically detecting the appropriate metric for a guided image search task.