Learning Output Kernels with Block Coordinate Descent

  title={Learning Output Kernels with Block Coordinate Descent},
  author={Francesco Dinuzzo and Cheng Soon Ong and Peter V. Gehler and Gianluigi Pillonetto},
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… CONTINUE READING
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