Minimum density hyperplanes
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1068
- Type
- D - Journal article
- DOI
-
-
- Title of journal
- Journal of Machine Learning Research
- Article number
- -
- First page
- 1
- Volume
- 17
- Issue
- 156
- ISSN
- 1532-4435
- Open access status
- Compliant
- Month of publication
- September
- 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
-
2
- Research group(s)
-
-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work proposes a novel hyperplane classifier for clustering and semi-supervised classification. The algorithm minimizes the integral of the empirical probability density function along hyperplanes while a projection pursuit formulation of the associated optimization problem allows us to find minimum density hyperplanes efficiently in practice. The work laid the foundations for subsequent papers published in “Statistics and Computing” (https://link.springer.com/article/10.1007/s11222-018-9814-6) and “IEEE Transactions on Pattern Analysis and Machine Intelligence” (https://ieeexplore.ieee.org/abstract/document/7569106).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
