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2010
ACM

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

10 years 1 months ago
Decomposing the tensor kernel support vector machine for neuroscience data with structured labels
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. Keywords Tensor kernel · Support vector machine · Decomposition · fMRI
David R. Hardoon, John Shawe-Taylor
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where ML
Authors David R. Hardoon, John Shawe-Taylor
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