Data Visualization with Structural Control of Global Cohort and Local Data Neighborhoods
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 64058859
- Type
- D - Journal article
- DOI
-
10.1109/TPAMI.2017.2715806
- Title of journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Article number
- -
- First page
- 1323
- Volume
- 40
- Issue
- 6
- ISSN
- 0162-8828
- Open access status
- Compliant
- Month of publication
- June
- 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
-
2
- Research group(s)
-
A - Computer Science
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This paper studies dimensionality reduction (DR): a canonical machine learning problem identified by The Royal Society’s machine learning project 2017. First to recognise a critical drawback of the commonly used local DR techniques and proposed an effective mathematical solution.
Invited talks at STFC Rutherford-Appleton Lab; Shanghai Jiaotong University.
Enabled funding GBP300,000 (KTP 12390) with Thornton & Lowe.
Served as the theoretical foundation of a recent work IEEE TKDE (DOI: 10.1109/TKDE.2019.2913379, acceptance rate 14%)."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
