Geometric Matrix Completion: A Functional View
Machine Learning
2020-10-01 v1 Computer Vision and Pattern Recognition
Social and Information Networks
Machine Learning
Abstract
We propose a totally functional view of geometric matrix completion problem. Differently from existing work, we propose a novel regularization inspired from the functional map literature that is more interpretable and theoretically sound. On synthetic tasks with strong underlying geometric structure, our framework outperforms state of the art by a huge margin (two order of magnitude) demonstrating the potential of our approach. On real datasets, we achieve state-of-the-art results at a fraction of the computational effort of previous methods. Our code is publicly available at https://github.com/Not-IITian/functional-matrix-completion
Cite
@article{arxiv.2009.14343,
title = {Geometric Matrix Completion: A Functional View},
author = {Abhishek Sharma and Maks Ovsjanikov},
journal= {arXiv preprint arXiv:2009.14343},
year = {2020}
}
Comments
Accepted at GRL workshop, ICML'20. Code: \url{https://github.com/Not-IITian/functional-matrix-completion}