Preconditioned conjugate gradient algorithms for graph regularized matrix completion

Dong, Shuyu;Absil, Pierre-Antoine;Gallivan, Kyle A.
(2019) 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning(ESANN 2019) — Location: Bruges (24.April.2019)

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Authors
  • Dong, ShuyuUCLouvain
    Author
  • Absil, Pierre-AntoineUCLouvain
    Author
  • Gallivan, Kyle A.Florida State University, Department of Mathematics, Tallahassee FL 32306-4510, USA
    Author
Abstract
Low-rank matrix completion is the problem of recovering the missing entries of a data matrix by using the assumption that a good low-rank approximation to the true matrix is possible. Much attention has been paid recently to exploiting correlations between the column/row entities through side information to improve the matrix completion quality. In this paper, we propose an efficient algorithm for solving the low-rank matrix completion with graph-based regularizers. Experiments on synthetic data show that our approach achieves significant speedup compared to the alternating minimization scheme.
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Dong, S., Absil, P.-A., & Gallivan, K. A. (2019). Preconditioned conjugate gradient algorithms for graph regularized matrix completion. ESANN 2019 Proceedings, p. 239-244. https://hdl.handle.net/2078.5/253938