Decomposing the Tensor Kernel Support Vector Machine for Neuroscience Data with Structure Labels
David Hardoon and John Shawe-Taylor
Machine Learning Journal: Special Issue on Learning From Multiple Sources
The tensor kernel has been used across the machine learning literature for a num- ber of purposes and applications, due to its ability to incorporate samples from multiple sources into a joint kernel defined feature space. Despite these uses, there have been no attempts made towards investigating the resulting tensor weight in respect to the contribu- tion of the individual tensor sources. Motivated by the increase in the current availability of Neuroscience data, specifically for two-source analyses, we propose a novel approach for decomposing the resulting tensor weight into its two components without accessing the feature space. We demonstrate our method and give experimental results on paired fMRI image-stimuli data.