Inferring Latent Task Structure for Multi-Task Learning by Multiple Kernel Learning
Christian Widmer, Nora C Toussaint, Yasemin Altun and Gunnar Raetsch
In: Machine Learning in Computational Biology (MLCB) 2009, 10-11 December 2009, Whistler, Canada.
Background: The lack of sufficient training data is the limiting factor for many Machine Learning applications in
Computational Biology. If data is available for several different but related problem domains, Multitask Learning
algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can
be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining
information from several tasks requires careful consideration of the degree of similarity between tasks. Our
proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning
classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the
recently published q-Norm MKL algorithm.
Results: We demonstrate the performance of our method on two problems from Computational Biology. First, we
show that our method is able to improve performance on a splice site dataset with given hierarchical task
structure by refining the task relationships. Second, we consider an MHC-I dataset, for which we assume no
knowledge about the degree of task relatedness. Here, we are able to learn the task similarities ab initio along with
the Multitask classifiers. In both cases, we outperform baseline methods that we compare against.
Conclusions: We present a novel approach to Multitask Learning that is capable of learning task similarity along
with the classifiers. The framework is very general as it allows to incorporate prior knowledge about tasks
relationships if available, but is also able to identify task similarities in absence of such prior information. Both
variants show promising results in applications from Computational Biology.