Improved data visualisation through nonlinear dissimilarity modelling
- Submitting institution
-
Birmingham City University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11Z_OP_D2034
- Type
- D - Journal article
- DOI
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10.1016/j.patcog.2017.07.016
- Title of journal
- Pattern Recognition
- Article number
- -
- First page
- 76-88
- Volume
- 73
- Issue
- -
- ISSN
- 0031-3203
- Open access status
- Technical exception
- Month of publication
- -
- 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
-
-
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is an extension to the paper ‘Improved data visualisation through multiple dissimilarity modelling’ incorporating a more complex learning algorithm allowing for representations of complex datasets without prior domain knowledge. The experiments presented utilise standard machine learning datasets to evidence rigour and clear understanding of the benefits of the techniques with additional numerical evaluation. The nonlinear interpolation approach generates a mapping allowing for new data to be visualised whilst also facilitating the adaptation of dissimilarity measures to suit any application domain.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
