Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2021
Abstract
In this paper, we bridge the gap between the state-of-the-art theoretical results for matrix completion with the nuclear norm and their equivalent in \textit{inductive matrix completion}: (1) In the distribution-free setting, we prove bounds improving the previously best scaling of \widetilde{O}(rd2) to \widetilde{O}(d3/2√r), where d is the dimension of the side information and rr is the rank. (2) We introduce the (smoothed) \textit{adjusted trace-norm minimization} strategy, an inductive analogue of the weighted trace norm, for which we show guarantees of the order \widetilde{O}(dr) under arbitrary sampling. In the inductive case, a similar rate was previously achieved only under uniform sampling and for exact recovery. Both our results align with the state of the art in the particular case of standard (non-inductive) matrix completion, where they are known to be tight up to log terms. Experiments further confirm that our strategy outperforms standard inductive matrix completion on various synthetic datasets and real problems, justifying its place as an important tool in the arsenal of methods for matrix completion using side information.
Keywords
Matrix Completion, Recommender Systems, Distribution-sensitive Learning, Statistical Learning Theory, Nuclear Norm
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 35th Conference on Neural Information Processing System (NeurIPS 2021), Virtual Conference, December 6-12
Volume
34
First Page
25540
Last Page
25552
ISBN
9781713845393
City or Country
Virtual Conference
Citation
LEDENT, Antoine; ALVES, RODRIGO; LEI, Yunwen; and KLOFT, Marius.
Fine-grained generalization analysis of inductive matrix completion. (2021). Proceedings of the 35th Conference on Neural Information Processing System (NeurIPS 2021), Virtual Conference, December 6-12. 34, 25540-25552.
Available at: https://ink.library.smu.edu.sg/sis_research/7201
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://proceedings.neurips.cc/paper/2021/hash/d6428eecbe0f7dff83fc607c5044b2b9-Abstract.html
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons