Tensor rank learning in CP decomposition via convolutional neural network
Tensor factorization is a useful technique for capturing the high-order interactions in data
analysis. One assumption of tensor decompositions is that a predefined rank should be
known in advance. However, the tensor rank prediction is an NP-hard problem. The
CANDECOMP/PARAFAC (CP) decomposition is a typical one. In this paper, we propose two
methods based on convolutional neural network (CNN) to estimate CP tensor rank from
noisy measurements. One applies CNN to the CP rank estimation directly. The other one …
analysis. One assumption of tensor decompositions is that a predefined rank should be
known in advance. However, the tensor rank prediction is an NP-hard problem. The
CANDECOMP/PARAFAC (CP) decomposition is a typical one. In this paper, we propose two
methods based on convolutional neural network (CNN) to estimate CP tensor rank from
noisy measurements. One applies CNN to the CP rank estimation directly. The other one …