TRAINING ASYMMETRIC KERNELS OF DETERMINANTAL POINT PROCESSES

摘要

Determinantal Point Process-based predictions are provided by training an asymmetric kernel of a Determinantal Point Process (DPP) from a training data set by calculating an inverse matrix of a sum of the asymmetric kernel and a first identity matrix, the calculating using an inverse of a sum of the first identity matrix and a symmetric positive semidefinite matrix, a concatenated matrix made from a first matrix and a second matrix and a second identity matrix, the asymmetric kernel including the symmetric positive semidefinite matrix and a skewed-symmetric matrix, the skewed-symmetric matrix being calculated from the first matrix and the second matrix, to produce a prediction model, and outputting the asymmetric kernel as at least a part of the prediction model to make a prediction.