Learning in the model space for cognitive fault diagnosis
- Submitting institution
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The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 24113101
- Type
- D - Journal article
- DOI
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10.1109/TNNLS.2013.2256797
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- 6515601
- First page
- 124
- Volume
- 25
- Issue
- 1
- ISSN
- 2162-237X
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- 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
- The paper proposes a new, computationally cheap, yet effective method for time series clustering or classification that can naturally handle long multivariate time series of variable length. The grouping can be discovered in an on-line fashion without any prior knowledge. This is significant because time series clustering or classification of such data appears in many real-world tasks (e.g. video annotation or classification) and current approaches are either computationally prohibitive or inadequate when long (potentially) high-dimensional time series are considered. The approach has been built upon by others, e.g. Witali Aswolinskiy - PhD thesis (2018), Neurocomputing (2017), Neural Processing Letters (2018).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -