  We address the problem of filling missing entries in a kernel Gram matrix,
given a related full Gram matrix. We attack this problem from the viewpoint of
regression, assuming that the two kernel matrices can be considered as
explanatory variables and response variables, respectively. We propose a
variant of the regression model based on the underlying features in the
reproducing kernel Hilbert space by modifying the idea of kernel canonical
correlation analysis, and we estimate the missing entries by fitting this model
to the existing samples. We obtain promising experimental results on gene
network inference and protein 3D structure prediction from genomic datasets. We
also discuss the relationship with the em-algorithm based on information
geometry.
