Orderly subspace clustering
- Submitting institution
-
University of Greenwich
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 30486
- Type
- E - Conference contribution
- DOI
-
10.1609/aaai.v33i01.33015264
- Title of conference / published proceedings
- Proceedings of the AAAI Conference on Artificial Intelligence
- First page
- 5264
- Volume
- 0
- Issue
- -
- ISSN
- 2159-5399
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2019
- 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
-
5
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Existing semi-supervised subspace clustering can use pairwise supervision as guidance to explore the relationships among data: however, pairwise supervision is often difficult to achieve. To remedy this, in this paper we make the first attempt towards utilising naturally occurring orderly relationships as a novel supervision technique, and propose an orderly subspace clustering (OSC) approach. Compared with pairwise subspace clustering approaches, OSC can not only preserve the true data structure beyond what existing methods offer, but also achieves more accurate clustering against state of the art approaches. In 2019, the acceptance rate for AAAI was 16.2%.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
