The focus here is on imputation of missing data from tensors. (also known as multi-way arrays), which are high-order generaliza- tions of matrices frequently ...

Abstract: Completion or imputation of three-way data arrays with missing entries is a basic problem encountered in various areas, including bio-informatics, ...

The rank of the tensor estimate is controlled by a novel regularization on the factors of its PARAFAC decomposition. Such a regularization is inspired by a ...

The rank of the tensor estimate is controlled by a novel regularization on the factors of its PARAFAC decomposition. Such a regularization is inspired by a ...

Our goal is to find a low rank matrix which has the smallest sum of singular values (i.e., nuclear norm) while its non-missing entries are the same as non- ...

In this paper, we formulate the missing data imputation problem in spatiotemporal traffic data in a low-rank tensor completion (LRTC) framework.

A nonparametric approach for estimating the marginal cumulative distribution function at each time point is proposed and used to test for factor effects and ...

A widely applied imputation method is low-rank matrix/tensor completion; however, the low-rank assumption only preserves the global structure while ignores ...

This paper proposes an efficient algorithm (HOLRR) to handle regression tasks where the outputs have a tensor structure. We formulate the regression.

The imputation approach presented in this paper builds on a novel regularizer accounting for the tensor rank, that relies on redefining the matrix nuclear norm ...