Dimensionality Reduction Techniques

Dimensionality reduction simplifies a dataset by reducing the number of features while keeping as much important information as possible.

The following techniques are used for dimensionality reduction.

• Principal Component Analysis: Combines correlated features into fewer new variables that still capture most of the original data's variation.
• Linear Discriminant Analysis: Reduces dimensions while specifically preserving separation between known categories.
• t-SNE: Reduces high-dimensional data into two or three dimensions, mainly used for visualizing complex data in an understandable way.
• Autoencoders: Neural networks that learn a compressed version of the data, then attempt to reconstruct the original from that compressed form.
• Feature Elimination: Manually or automatically removes features that add little value, based on correlation or importance scores.