What is the role of streaming data in modern dimensionality reduction?

Streaming DR adapts representations continuously as new data arrives.

In streaming environments, data distributions may shift over time (concept drift). Dimensionality reduction methods must update embeddings without retraining from scratch. Techniques like online PCA, stochastic gradient-based autoencoders, and reservoir sampling help maintain updated low-dimensional representations. This is essential in real-time analytics and monitoring systems.