An automatic taxonomy of galaxy morphology using unsupervised machine learning
- Submitting institution
-
University of Hertfordshire
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13604578
- Type
- D - Journal article
- DOI
-
10.1093/mnras/stx2351
- Title of journal
- Monthly Notices of the Royal Astronomical Society
- Article number
- -
- First page
- 1108
- Volume
- 473
- Issue
- 1
- ISSN
- 0035-8711
- Open access status
- Not compliant
- Month of publication
- September
- 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
-
3
- Research group(s)
-
-
- Citation count
- 23
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The research
site https://www.altmetric.com/details/26291170 reported the Attention Score for the paper is
now 50, which means the paper is in the top 5% of all research outputs scored by Altmetric. The first author has been employed by Microsoft research as an applied machine learning scientist after leaving UH.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -