Adaptive spectrum transformation by topology preservation on indefinite proximity data
- Submitting institution
-
Staffordshire University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 6795
- Type
- D - Journal article
- DOI
-
10.1016/j.patrec.2017.08.006
- Title of journal
- Pattern Recognition Letters
- Article number
- -
- First page
- 59-67
- Volume
- 98
- Issue
- -
- ISSN
- 0167-8655
- Open access status
- Deposit exception
- Month of publication
- October
- Year of publication
- 2017
- URL
-
https://www.sciencedirect.com/science/article/abs/pii/S0167865517302623?via%3Dihub
- 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
-
1
- Research group(s)
-
B - Centre for Smart Systems, AI and Cybersecurity (CSSAIC)
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Similarity matrices are important in applications including natural language processing, information retrieval, bioinformatics, and computer vision. The significance of this paper is that it presents a method that ensures consistency with the underlying data. This work formed the basis for a continuing collaboration with Atieh Clinic Neuroscience Centre (https://atiehneurolab.com/en) and the University of Tehran. The main author, Khadijeh, subsequently achieved a postdoctoral position at INRIA Sud-Ouest, France.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -