Type
Conference PaperKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2019-01-18Online Publication Date
2019-01-18Print Publication Date
2019Permanent link to this record
http://hdl.handle.net/10754/631384
Metadata
Show full item recordAbstract
We present an overview of inequality-constrained matrix completion, with a particular focus on alternating least-squares (ALS) methods. The simple and seemingly obvious addition of inequality constraints to matrix completion seems to improve the statistical performance of matrix completion in a number of applications, such as collaborative filtering under interval uncertainty, robust statistics, event detection, and background modelling in computer vision. An ALS algorithm MACO by Marecek et al. outperforms others, including Sparkler, the implementation of Li et al. Code related to this paper is available at: http://optml.github.io/ac-dc/.Citation
Marecek J, Richtarik P, Takac M (2019) Matrix Completion Under Interval Uncertainty: Highlights. Lecture Notes in Computer Science: 621–625. Available: http://dx.doi.org/10.1007/978-3-030-10997-4_38.Sponsors
The work of JM received funding from the European Union’s Horizon 2020 Programme (Horizon2020/2014-2020) under grant agreement No. 688380. The work of MT was partially supported by the U.S. National Science Foundation, under award numbers NSF:CCF:1618717, NSF:CMMI:1663256, and NSF:CCF:1740796. PR acknowledges support from KAUST Faculty Baseline Research Funding Program.Publisher
Springer NatureConference/Event name
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018Additional Links
https://link.springer.com/chapter/10.1007%2F978-3-030-10997-4_38ae974a485f413a2113503eed53cd6c53
10.1007/978-3-030-10997-4_38