Dimensionality Reduction

Dimensionality reduction transforms high-dimensional data into lower dimensions while preserving essential information. This makes data more manageable for visualization and analysis.

Common approaches include Principal Component Analysis (PCA), which finds principal components that capture data variance, Autoencoders that compress data with neural networks, and t-SNE which preserves local relationships for visualization. These techniques help reduce noise and overfitting while highlighting key patterns.