The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Low-rank matrix completion using alternating minimization
Abstract
Alternating minimization represents a widely applicable and empirically successful approach for finding low-rank matrices that best fit the given data. For example, for the problem of low-rank matrix completion, this method is believed to be one of the most accurate and efficient, and formed a major component of the winning entry in the Netflix Challenge [17].
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Sparse Principal Component Analysis
Zou, H., Hastie, T., & Tibshirani, R. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x113430 · Cited by 3,152
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Jain, P., & Kar, P. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000058 · Cited by 364
Self-citationNonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
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Complete Dictionary Recovery Over the Sphere I: Overview and the Geometric Picture
Sun, J., Qu, Q., & Wright, J. (2017) · IEEE Transactions on Information Theory · doi:10.1109/tit.2016.2632162 · Cited by 213
Low-Rank Matrix Completion for Topological Interference Management by Riemannian Pursuit
Shi, Y., Zhang, J., & Letaief, K. B. (2016) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2016.2544758 · Cited by 56
Subspace Estimation from Unbalanced and Incomplete Data Matrices: 𝓁2, ∞ Statistical Guarantees
Cai, C., Li, G., Chi, Y., Poor, H. V., & Chen, Y. (2021) · The Annals of Statistics · doi:10.1214/20-aos1986 · Cited by 37
Metadata
- Authors
- Jain, P., Netrapalli, P., & Sanghavi, S.
- Venue
- Proceedings of the forty-fifth annual ACM symposium on Theory of Computing
- Publisher
- ACM
- Published
- 2013-06
- Pages
- 665-674
- OpenAlex
- W2106005123
- Cited by
- 817
- References
- 2
- FWCI
- 111.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Jain, P., Netrapalli, P., & Sanghavi, S. (2013). Low-rank matrix completion using alternating minimization. Proceedings of the forty-fifth annual ACM symposium on Theory of Computing. https://doi.org/10.1145/2488608.2488693
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
