Zusammenfassung
Gaussian Processes are widely used for regression tasks. A known limitation
in the application of Gaussian Processes to regression tasks is that the
computation of the solution requires performing a matrix inversion. The
solution also requires the storage of a large matrix in memory. These factors
restrict the application of Gaussian Process regression to small and moderate
size data sets. We present an algorithm based on empirically determined subset
selection that works well on both real world and synthetic datasets. On the
synthetic and real world datasets used in this study, the algorithm
demonstrated sub-linear time and space complexity. The correctness proof for
the algorithm is also presented.
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