Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation
- Submitting institution
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University of Aberdeen
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 180305103
- Type
- E - Conference contribution
- DOI
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-
- Title of conference / published proceedings
- Advances in Neural Information Processing Systems 27 (NIPS 2014)
- First page
- 3590
- Volume
- -
- Issue
- -
- ISSN
- 1049-5258
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2014
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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2
- Research group(s)
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-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper addresses the general problem of constrained hidden Markov model (HMM) at global level where the states of the HMM are globally constrained. The paper proposes a new convex optimization approximation to constrained HMMs. This significantly contributed to the HMM community because the approach provides a global solution to HMMs comparing to the Viterbi algorithm . This model has been the baseline model for non-intrusive load monitoring employing HMM as it was incorporated into the software nilmtk-contrib (https://tinyurl.com/y3odkyzy).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
