I would like to announce Diagonalizations.jl, a new package for multivariate statistics/signal processing. Currently it implements the following linear filters:
| Acronym | Full Name | Datasets ( m ) | Observations ( k ) |
|---|---|---|---|
| PCA | Principal Component Analysis | 1 | 1 |
| Whitening | Whitening (Sphering) | 1 | 1 |
| MCA | Maximum Covariance Analysis | 2 | 1 |
| CCA | Canonical Correlation Analysis | 2 | 1 |
| gMCA | generalized MCA | >1 | 1 |
| gCCA | generalized CCA | >1 | 1 |
| CSP | Common Spatial Pattern | 1 | 2 |
| CSTP | Common Spatio-Temporal Pattern | 1 | >1 |
| AJD | Approximate Joint Diagonalization | 1 | >1 |
| mAJD | multiple AJD | >1 | >1 |
It is thoroughly tested (for real data input for the moment being) and documented.
As compared to MultivariateStats.jl this package supports :
- the
dimskeyword, - built-in shrinkage covariance matrix estimations throught the CovarianceEstimation package,
- built-in average covariance estimations with metrics for the manifold of positive definite matrices throught the PosDefManifold package,
- automatic procedures to set the subspace dimension upon construction,
- approximate diagonalization procedures for the case
m≥2andk≥2(see table above).