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Classification tasks in computer vision and brain-computer interface research have presented several applications such as biometrics and cognitive training. However, determining suitable data representation has been challenging, and recent approaches have deviated from the familiar form of one vector for each data sample. This paper considers a kernel between vector sets, the mean polynomial kernel, motivated by recent studies where data are approximated by linear subspaces, in particular, methods on Grassmann manifolds. The kernel supports vector sequences as input. We discuss how the kernel can be associated with the Grassmann Projection kernel, and provide experimental results showing how it outperforms existing subspace-based methods on Grassmann manifolds.

Mean Polynomial Kernel and Its Application to Vector Sequence Recognition