What is the role of sparsity in dimensionality reduction?

Sparsity improves interpretability and efficiency by reducing active features.

Sparse dimensionality reduction techniques enforce that only a subset of features contribute to each component. This improves interpretability and computational efficiency. Methods include Lasso-based PCA variants and sparse autoencoders.