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The Algebraic Combinatorial Approach for Low-Rank Matrix Completion

Machine Learning 2014-08-20 v4 Numerical Analysis Algebraic Geometry Combinatorics Machine Learning

Abstract

We present a novel algebraic combinatorial view on low-rank matrix completion based on studying relations between a few entries with tools from algebraic geometry and matroid theory. The intrinsic locality of the approach allows for the treatment of single entries in a closed theoretical and practical framework. More specifically, apart from introducing an algebraic combinatorial theory of low-rank matrix completion, we present probability-one algorithms to decide whether a particular entry of the matrix can be completed. We also describe methods to complete that entry from a few others, and to estimate the error which is incurred by any method completing that entry. Furthermore, we show how known results on matrix completion and their sampling assumptions can be related to our new perspective and interpreted in terms of a completability phase transition.

Keywords

Cite

@article{arxiv.1211.4116,
  title  = {The Algebraic Combinatorial Approach for Low-Rank Matrix Completion},
  author = {Franz J. Király and Louis Theran and Ryota Tomioka},
  journal= {arXiv preprint arXiv:1211.4116},
  year   = {2014}
}

Comments

37 pages, with an appendix by Takeaki Uno

R2 v1 2026-06-21T22:40:04.401Z