What is the difference between batch and incremental dimensionality reduction?

Batch methods process all data at once, while incremental methods update models progressively.

Batch dimensionality reduction methods like PCA compute transformations using the entire dataset simultaneously. This requires storing and processing all data in memory. Incremental methods such as Incremental PCA or online SVD update the model using streaming or mini-batch data, making them suitable for real-time or large-scale systems. Incremental learning introduces slight approximation error but significantly improves scalability and adaptability.