Table 7.
Features of open-source dimensionality reduction toolboxes regarding visualization tools, principal and usage programming language, availability of documentation, number of citations, and support by updates at least once per year.
| Toolbox, version | Visuali- | Language | Documen- | Cited | Support | Methods |
|---|---|---|---|---|---|---|
| zation | tation | |||||
| DataHigh v.1.2 | + | MATLAB | + | <30 | In part | FA, GPFA, LDA, PCA |
| DCA v1.0 | − | MATLAB | In part | <30 | In part | DCA |
| Python | ||||||
| dPCA v0.1 | + | MATLAB | + | <300 | + | dPCA, PCA |
| Python | ||||||
| GPFA v.2.03 | + | MATLAB | In part | >300 | In part | FA, PCA, pPCA, GPFA |
| seqNMF | + | MATLAB | + | <30 | + | NMF, PCA |
| tensor-demo | + | MATLAB | + | <30 | + | TCA |
| Python | ||||||
| tensortools v0.3.0 | + | Python | + | <30 | + | ccpTD, nnTCA |
| TD-GPFA v3.0 | + | MATLAB | In part | <30 | In part | FA, GPFA, PCA, pPCA |
ccpTD, coupled canonical polyadic Tensor Decomposition; DCA, Distance Covariances Analysis; (GP)FA, (Gaussian Process) Factor Analysis; LDA, Fisher's Linear Discriminant Analysis; NMF, Non-negative Matrix Factorization; (d,p)PCA, (demixed, probabilistic) Principal Component Analysis; (nn)TCA, (non-negative) Tensor Component Analysis. Bold values indicate the number of citations higher than 90.