Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction
This work addresses the problem of predicting athletic performance from incomplete data, representing an incremental improvement in matrix completion methods.
The paper tackles the problem of low-rank matrix completion for estimating missing entries, achieving results that outperform state-of-the-art nuclear norm methods in both accuracy and computational efficiency, as demonstrated in simulations and athletic performance prediction tasks.
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.