Rank-One Matrix Completion With Automatic Rank Estimation via L1-Norm Regularization
- Submitting institution
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The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2587
- Type
- D - Journal article
- DOI
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10.1109/TNNLS.2017.2766160
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 4744
- Volume
- 29
- Issue
- 10
- ISSN
- 2162-2388
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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C - Machine Learning
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents the state-of-the-art approximation method for accurate rank estimation and matrix recovery. This has practical importance in areas such as matrix completion, low-rank matrix approximation, and feature extraction/selection from tensors, receiving many citations by top journals such as TNNLS, TCyb, and TSP, and key conferences such as KDD. It led to a grant of £100,730 awarded by EPSRC (EP/R014507/1) on learning low-rank representations for brain imaging data.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -