Abstract
Data imputation is the most popular method of dealing with missing values, but in most real life applications, large missing data can occur and it is difficult or impossible to evaluate whether data has been imputed accurately (lack of ground
truth). This paper addresses these issues by proposing an effective and simple principal component based method for determining whether individual data features can be accurately imputed - feature imputability. In particular, we establish a strong linear relationship between principal component loadings and feature imputability, even in the presence of extreme missingness and lack of ground truth. This work will have important implications in practical data imputation strategies.
truth). This paper addresses these issues by proposing an effective and simple principal component based method for determining whether individual data features can be accurately imputed - feature imputability. In particular, we establish a strong linear relationship between principal component loadings and feature imputability, even in the presence of extreme missingness and lack of ground truth. This work will have important implications in practical data imputation strategies.
Original language | English |
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Number of pages | 5 |
Publication status | Accepted/In press - 2 Jul 2020 |
Event | 37th International Conference on Machine Learning (ICML): The Art of Learning with Missing Values (ARTEMISS) Workshop - Vienna, Austria Duration: 17 Jul 2020 → 17 Jul 2020 https://artemiss-workshop.github.io/ |
Conference
Conference | 37th International Conference on Machine Learning (ICML): The Art of Learning with Missing Values (ARTEMISS) Workshop |
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Abbreviated title | ICML 2020: ARTEMISS 2020 |
Country/Territory | Austria |
City | Vienna |
Period | 17/07/20 → 17/07/20 |
Internet address |
Keywords
- Missing data
- data imputation
- principal component analysis PCA
- NIPALS
- dementia
- Alzheimer's disease
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Kongfatt Wong-Lin
- School of Computing, Eng & Intel. Sys - Professor
- Faculty Of Computing, Eng. & Built Env. - Full Professor
Person: Academic