A quantum Jensen-Shannon graph kernel for unattributed graphs
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 541
- 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
- February
- Year of publication
- 2015
- 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
- This paper introduced a new class of kernels for graph-based machine learning based on continuous-time quantum walks, giving new insight into how quantum mechanics can inspire the design of more effective classical algorithms running on standard computers. This work led to 1) an invited talk (https://sites.google.com/site/feast2014/invited-speakers ) at the FEAST workshop (70 participants https://iapr.org/docs/newsletter-2014-04.pdf), co-located with the 2014 IEEE International Conference on Pattern Recognition (https://iapr.org/archives/icpr2014/), 2) the Best Student Paper Award at ICIAP 2015 (https://www.iciap2015.eu/program/awards.html) and 3) a visiting scholarship in July 2019 (https://www.unive.it/pag/17439/)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -