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    Matrix Completion Under Interval Uncertainty: Highlights

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    Type
    Conference Paper
    Authors
    Marecek, Jakub
    Richtarik, Peter cc
    Takac, Martin
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-01-18
    Online Publication Date
    2019-01-18
    Print Publication Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/631384
    
    Metadata
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    Abstract
    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 Nature
    Journal
    Machine Learning and Knowledge Discovery in Databases
    Conference/Event name
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018
    DOI
    10.1007/978-3-030-10997-4_38
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-030-10997-4_38
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-10997-4_38
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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