What is the role of distributed computing in dimensionality reduction?

Distributed computing enables dimensionality reduction on datasets that exceed single-machine memory limits.

Distributed dimensionality reduction partitions data across multiple nodes and performs parallel computations for matrix operations, eigen decomposition, or gradient-based optimization. Frameworks like Spark MLlib and distributed linear algebra libraries allow PCA and SVD to scale across clusters. The main challenge is minimizing communication overhead between nodes while preserving numerical stability and convergence consistency.