Accelerated Dempster Shafer Using Tensor Train Representation
摘要
We propose a tensor train based data structure to accelerate the calculation of Dempster-Shafer operations such as belief and Dempster’s rule of combination. This approach relies on the fact that the matrix representation of these operators possess rank-1 tensor network decompositions, allowing for far more efficient calculations in tensor train format. Numerical experiments demonstrate the superior performance of the proposed method in computing Dempster-Shafer quantities.