Article

Machine Learning

, Volume 79, Issue 1, pp 29-46

First online:

Decomposing the tensor kernel support vector machine for neuroscience data with structured labels

  • David R. HardoonAffiliated withCentre for Computational Statistics and Machine Learning, Department of Computer Science, University College London Email author 
  • , John Shawe-TaylorAffiliated withCentre for Computational Statistics and Machine Learning, Department of Computer Science, University College London

Abstract

The tensor kernel has been used across the machine learning literature for a number 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 contribution 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.

Tensor kernel Support vector machine Decomposition fMRI