Tensor based representation and analysis of the electronic healthcare record data

The paper addresses the problem of multidimensional data representation and analysis in electronic healthcare records. Our methodology is based on the best all-rank tensor decomposition which allows data compression and simultaneous classification in the tensor subspaces. Experiments were run on the MRI brain signals. The obtained results show high compression ratios which do not sacrifice reconstruction accuracies. Also, the method allows fast and highly discriminative matching of the MRI signals to the models built with the proposed method.