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キーワード:
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要旨:
We propose a method to learn simultaneously a vector-valued function and a
kernel between its components. The obtained kernel can be used both to improve
learning performance and to reveal structures in the output space which may be
important in their own right. Our method is based on the solution of a suitable
regularization problem over a reproducing kernel Hilbert space of vector-valued
functions. Although the regularized risk functional is non-convex, we show that
it is invex, implying that all local minimizers are global minimizers. We
derive a block-wise coordinate descent method that efficiently exploits the
structure of the objective functional. Then, we empirically demonstrate that
the proposed method can improve classification accuracy. Finally, we provide a
visual interpretation of the learned kernel matrix for some well known
datasets.
