#### Dimensionality Reduction

Dimensionality reduction transforms high-dimensional data into 2D or 3D visualizations while preserving the structure and relationships between documents.

#### Algorithms

- PCA: Principal Component Analysis finds linear patterns. Fast and interpretable, best for linear relationships.
- t-SNE: Preserves local structure, revealing clusters. Good for visualization but computationally intensive.
- UMAP: Balances local and global structure. Faster than t-SNE with better preservation of overall data topology.

#### Usage

Select a semantic feature space (words, n-grams, or embeddings), then choose a reduction method. Adjust parameters (perplexity, neighbors, dimensions) based on data size and structure. Use for visual exploration before clustering.

Learn More: scikit-learn Manifold Learning