  Recent studies utilize multiple kernel learning to deal with incomplete-data
problem. In this study, we introduce new methods that do not only complete
multiple incomplete kernel matrices simultaneously, but also allow control of
the flexibility of the model by parameterizing the model matrix. By imposing
restrictions on the model covariance, overfitting of the data is avoided. A
limitation of kernel matrix estimations done via optimization of an objective
function is that the positive definiteness of the result is not guaranteed. In
view of this limitation, our proposed methods employ the LogDet divergence,
which ensures the positive definiteness of the resulting inferred kernel
matrix. We empirically show that our proposed restricted covariance models,
employed with LogDet divergence, yield significant improvements in the
generalization performance of previous completion methods.

Machine Learning;Machine Learning