Python package corexcontinuous
Return components/latent factors that explain the most multivariate mutual information in the data under Linear Gaussian model. For comparison, PCA returns components explaining the most variance in the data.
Summary
| Count | 12 occurrences |
|---|---|
| State | Dead |
| Last occurred | |
| Habitening next | |
| Age | |
| Average | |
| Honeymoon | |
| Trend | None |
| In degree | 36 |
| Out degree | 90 |
| External links |