Abstract
Principal loading analysis is a dimension reduction method that discards variables which have only a small distorting effect on the covariance matrix. We complement principal loading analysis and propose to rather use a mix of both, the correlation and covariance matrix instead. Further, we suggest to use rescaled eigenvectors and provide updated algorithms for all proposed changes.
| Original language | English |
|---|---|
| Title of host publication | ICoMS 2021 |
| Subtitle of host publication | Proceedings of the 4th International Conference on Mathematics and Statistics |
| Publisher | Association for Computing Machinery |
| Pages | 27-34 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781450389907 |
| DOIs | |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 4th International Conference on Mathematics and Statistics, ICoMS 2021 - Virtual, Online, France Duration: 24 Jun 2021 → 26 Jun 2021 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Conference
| Conference | 4th International Conference on Mathematics and Statistics, ICoMS 2021 |
|---|---|
| Country/Territory | France |
| City | Virtual, Online |
| Period | 24/06/21 → 26/06/21 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy -
SDG 10 Reduced Inequalities
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