A quantum Jensen-Shannon graph kernel for unattributed graphs
- Submitting institution
-
University of York
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 54874389
- Type
- D - Journal article
- DOI
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10.1016/j.patcog.2014.03.028
- Title of journal
- Pattern Recognition
- Article number
- -
- First page
- 344
- Volume
- 48
- Issue
- 2
- ISSN
- 0031-3203
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2014
- 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
- 48
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Novel use of quantum Jensen–Shannon divergence as a means of measuring the information theoretic dissimilarity of graphs characterised in terms of the density matrices of continuous time quantum walks, and thus develop a novel graph kernel. Evaluated on standard graph datasets from both bioinformatics and computer vision. Appears in Pattern Recognition, which is influential and long established journal consistently in top percentiles for computer science.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -