- Logistic PCA
- allow for dealing with binary and categorical (discrete) input features
- Sparse PCA
- overcome a PCA weakness that PCs are usually linear combinations of all the input features by using linear combinations of just a few input variables
- Nonlinear PCAs
- Robust PCAs
- overcome the PCA weakness that PCA is sensitive to outliers in the data that cause large errors, e.g., weighted PCA.
- Probabilistic PCA
- a stochastic PCA variant (generative model) that explains the PCA process from a data generation perspective