Low-Rank Regression with Tensor Responses
Abstract
This paper proposes an efficient algorithm (HOLRR) to handle regression tasks where the outputs have a tensor structure. We formulate the regression problem as the minimization of a least square criterion under a multilinear rank constraint, a difficult non convex problem. HOLRR computes efficiently an approximate solution of this problem, with solid theoretical guarantees. A kernel extension is also presented. Experiments on synthetic and real data show that HOLRR computes accurate solutions while being computationally very competitive.
Cite
Text
Rabusseau and Kadri. "Low-Rank Regression with Tensor Responses." Neural Information Processing Systems, 2016.Markdown
[Rabusseau and Kadri. "Low-Rank Regression with Tensor Responses." Neural Information Processing Systems, 2016.](https://mlanthology.org/neurips/2016/rabusseau2016neurips-lowrank/)BibTeX
@inproceedings{rabusseau2016neurips-lowrank,
title = {{Low-Rank Regression with Tensor Responses}},
author = {Rabusseau, Guillaume and Kadri, Hachem},
booktitle = {Neural Information Processing Systems},
year = {2016},
pages = {1867-1875},
url = {https://mlanthology.org/neurips/2016/rabusseau2016neurips-lowrank/}
}