Joint Hypergraph Learning and Sparse Regression for Feature Selection
- Submitting institution
-
University of York
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 59954709
- Type
- D - Journal article
- DOI
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10.1016/j.patcog.2016.06.009
- Title of journal
- Pattern Recognition
- Article number
- -
- First page
- 291
- Volume
- 63
- Issue
- -
- ISSN
- 0031-3203
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2016
- 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
-
3
- Research group(s)
-
-
- Citation count
- 35
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper has proved influential in the machine learning domain, with work cited at IJCAI, AAAI and NIPS. It presents a novel unified framework for improved higher-order (beyond pairwise) structure estimation in feature selection. Pre-existing graph-based feature selection methods have utilized a static representation of the structure of the available data based on the Laplacian matrix of a simple graph.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -