Machine Learning

, Volume 79, Issue 1, pp 29–46

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

Authors

    • Centre for Computational Statistics and Machine Learning, Department of Computer ScienceUniversity College London
  • John Shawe-Taylor
    • Centre for Computational Statistics and Machine Learning, Department of Computer ScienceUniversity College London
Article

DOI: 10.1007/s10994-009-5159-x

Cite this article as:
Hardoon, D.R. & Shawe-Taylor, J. Mach Learn (2010) 79: 29. doi:10.1007/s10994-009-5159-x

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 kernelSupport vector machineDecompositionfMRI
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Copyright information

© The Author(s) 2009