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Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction

Blythe, DAJ; Theran, L; Kiraly, F; (2014) Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction. ArXiv: Ithaca, NY, USA. Green open access

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Abstract

This paper presents novel algorithms which exploit the intrinsic algebraic and combinatorial structure of the matrix completion task for estimating missing en- tries in the general low rank setting. For positive data, we achieve results out- performing the state of the art nuclear norm, both in accuracy and computational efficiency, in simulations and in the task of predicting athletic performance from partially observed data.

Type: Working / discussion paper
Title: Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction
Open access status: An open access version is available from UCL Discovery
Publisher version: https://arxiv.org/abs/1406.2864
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: stat.ML, stat.ML
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1517416
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