Seeland, M., Kramer, S., & Pfahringer, B. (2013). Model selection based product kernel learning for regression on graphs. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, Coimbra, Portugal, March 18 - 22, 2013 (pp. 136-143). New York, USA: ACM.
Permanent Research Commons link: http://hdl.handle.net/10289/7778
The choice of a suitable graph kernel is intrinsically hard and often cannot be made in an informed manner for a given dataset. Methods for multiple kernel learning offer a possible remedy, as they combine and weight kernels on the basis of a labeled training set of molecules to define a new kernel. Whereas most methods for multiple kernel learning focus on learning convex linear combinations of kernels, we propose to combine kernels in products, which theoretically enables higher expressiveness. In experiments on ten publicly available chemical QSAR datasets we show that product kernel learning is on no dataset significantly worse than any of the competing kernel methods and on average the best method available. A qualitative analysis of the resulting product kernels shows how the results vary from dataset to dataset.