Dimensionality Reduction
Techniques that reduce the number of features in data while preserving the most important information, making data easier to visualize and process.
Methods
PCA: Linear projection onto directions of maximum variance. t-SNE: Non-linear method for 2D/3D visualization. UMAP: Faster alternative to t-SNE with better global structure. Autoencoders: Neural network-based non-linear reduction.
Applications
Visualizing high-dimensional embedding spaces, preprocessing for ML models, noise reduction, compression, and exploratory data analysis.