What is the role of hybrid dimensionality reduction techniques?

Hybrid methods combine multiple dimensionality reduction techniques to balance strengths and weaknesses.

Hybrid approaches often combine linear and nonlinear methods, such as PCA followed by UMAP or autoencoders combined with clustering. PCA reduces noise and dimensionality first, making nonlinear methods more stable and scalable. These pipelines improve both performance and computational efficiency.