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Method Article
Emily Chia-Yu Su
(1) Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan. (2) Clinical Big Data Research Center, Taipei Medical University Hospital, 250 Wu-Xing Street, Taipei 11031, Taiwan. (3) Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan.
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4801-5159
License:
This work is licensed under a CC BY 4.0 License. Read Full License
We aimed to provide a resampling protocol for dimensional reduction resulting a few latent variables. The applicability focuses on but not limited for developing a machine learning prediction model in order to improve the number of sample size in relative to the number of candidate predictors. By this feature representation technique, one can improve generalization by preventing latent variables to overfit data used to conduct the dimensional reduction. However, this technique may warrant more computational capacity and time to conduct the procedure. The key stages consisted of derivation of latent variables from multiple resampling subsets, parameter estimation of latent variables in population, and selection of latent variables transformed by the estimated parameters.
Keywords
resampling method, dimensional reduction, latent variable, machine learning
Figure 1
This is a list of supplementary files associated with this preprint. Click to download.
- suppprotocolresdimer.pdf
Supplementary information for source code description
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Associated Publications
Prognostication for prelabor rupture of membranes and the time of delivery in nationwide insured women: development, validation, and deployment
by Herdiantri Sufriyana, Yu-Wei Wu, Emily Chia-Yu Su
An open repository of community-contributed protocols sponsored by Nature Research.
Learn more about Protocol Exchange
Method Article
Emily Chia-Yu Su
(1) Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan. (2) Clinical Big Data Research Center, Taipei Medical University Hospital, 250 Wu-Xing Street, Taipei 11031, Taiwan. (3) Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan.
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4801-5159
Version History
CURRENT STATUS: Posted
Version 1
Posted 13 Oct, 2021
No community comments so far
Metrics
Comments: 0
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
We aimed to provide a resampling protocol for dimensional reduction resulting a few latent variables. The applicability focuses on but not limited for developing a machine learning prediction model in order to improve the number of sample size in relative to the number of candidate predictors. By this feature representation technique, one can improve generalization by preventing latent variables to overfit data used to conduct the dimensional reduction. However, this technique may warrant more computational capacity and time to conduct the procedure. The key stages consisted of derivation of latent variables from multiple resampling subsets, parameter estimation of latent variables in population, and selection of latent variables transformed by the estimated parameters.
Figure 1
This is a list of supplementary files associated with this preprint. Click to download.
- suppprotocolresdimer.pdf
Supplementary information for source code description
Loading...
Associated Publications
Prognostication for prelabor rupture of membranes and the time of delivery in nationwide insured women: development, validation, and deployment
by Herdiantri Sufriyana, Yu-Wei Wu, Emily Chia-Yu Su