The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
An Overview of Low-Rank Matrix Recovery From Incomplete Observations
Open accessAbstract
Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations. This paper provides an overview of modern techniques for exploiting low-rank structure to perform matrix recovery in these settings, providing a survey of recent advances in this rapidly-developing field. Specific attention is paid to the algorithms most commonly used in practice, the existing theoretical guarantees for these algorithms, and representative practical applications of these techniques.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Beck, A., & Teboulle, M. (2009) · SIAM Journal on Imaging Sciences · doi:10.1137/080716542 · Cited by 11,952
Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516
Parikh, N., & Boyd, S. (2014) · Foundations and Trends® in Optimization · doi:10.1561/2400000003 · Cited by 4,102
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
Cited by
Federated Learning via Over-the-Air Computation
Yang, K., Jiang, T., Shi, Y., & Ding, Z. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2961673 · Cited by 1,084
“Sparse optimization and low-rank optimization have attracted much attention in machine learning, signal processing, high-dimensional statistics, as well as wireless communication [30], [31], [32], [33], [34].”
BackgroundExploring genetic interaction manifolds constructed from rich single-cell phenotypes
Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., & Weissman, J. S. (2019) · Science · doi:10.1126/science.aax4438 · Cited by 392
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
Chi, Y., Lu, Y. M., & Chen, Y. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2019.2937282 · Cited by 360
Generalized Sparse and Low-Rank Optimization for Ultra-Dense Networks
Shi, Y., Zhang, J., Chen, W., & Letaief, K. B. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700472 · Cited by 44
Data Shuffling in Wireless Distributed Computing via Low-Rank Optimization
Yang, K., Shi, Y., & Ding, Z. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2019.2912139 · Cited by 43
Metadata
- Authors
- Davenport, M. A., & Romberg, J.
- Venue
- IEEE Journal of Selected Topics in Signal Processing
- Published
- 2016-06
- Volume
- 10
- Issue
- 4
- Pages
- 608-622
- ISSN
- 1932-4553 (ISSN-L)
- 1941-0484
- OpenAlex
- W2268674159
- Cited by
- 439
- References
- 4
- FWCI
- 41.16
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An Overview of Low-Rank Matrix Recovery From Incomplete Observations. https://app.cochat.ai/papers/10.1109/jstsp.2016.2539100
- The paper
- Davenport, M. A., & Romberg, J. (2016). An Overview of Low-Rank Matrix Recovery From Incomplete Observations. IEEE Journal of Selected Topics in Signal Processing. https://doi.org/10.1109/jstsp.2016.2539100
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
