What is the difference between parametric and non-parametric dimensionality reduction?

Parametric methods learn a fixed function; non-parametric methods depend on entire dataset.

Parametric methods like autoencoders learn a mapping function that can generalize to new data. Non-parametric methods like t-SNE compute embeddings based on relationships within the full dataset, making them unable to easily embed new samples without retraining.