What are the different dimensionality reduction techniques?

What are the different dimensionality reduction techniques?

There are several dimensionality reduction techniques, including PCA, t-SNE, and UMAP. Would you like a comparison?

Yes, please compare PCA and t-SNE.

PCA is a linear technique that reduces dimensions by projecting data onto principal components, while t-SNE is non-linear and focuses on preserving local structures, making it better for visualizing high-dimensional data.

When should I use t-SNE over PCA?

Use t-SNE when you need to visualize complex data structures and relationships, especially in clustering tasks. PCA is better for preprocessing data for other algorithms.

Are there any other techniques I should consider?

Yes, UMAP is another popular technique that often outperforms t-SNE in terms of speed and preserving global structure. Would you like to know more about it?