• Non-negative Matrix Factorization (NMF) - Non-negative matrix factorization, an alternative approach to decomposition that assumes that the data and components are non-negative. NMF is an uncontrolled linear dimensionality reduction technique. In NMF, the original data (feature matrix) is split into multiple matrices (i.e., factorized) representing the hidden relationship between observations and their characteristics. NMF can be connected instead of PCA when the data matrix does not contain negative values. NMF does not provide an explained variance like PCA and other methods, so the best way to find the optimal value for n_components is to try a range of values.
• Truncated Singular Value Decomposition (TSVD) - Truncated singular value decomposition is similar to PCA. This method performs linear dimensionality reduction using a truncated singular value decomposition. Unlike PCA, this estimator does not center the data before computing the singular value decomposition and can work efficiently with sparse matrices.
https://medium.com/@deepak.engg.phd/dimensionality-reduction-with-scikit-learn-ee5d2b69225b