Intermediate · Data
Dimension reduction
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Techniques to reduce the number of features or variables in a dataset while retaining important information.
Technical Definition
Techniques to reduce the number of features or variables in a dataset while retaining important information.
How it works
Dimension reduction aims to decrease the number of features (dimensions) in a dataset, often by creating new, fewer features that capture most of the original data's variance or structure. This is commonly achieved through methods like Principal Component Analysis (PCA) or by transforming features into lower-dimensional embeddings. It helps in simplifying models, reducing computational cost, and mitigating the curse of dimensionality.
Related Concepts
- Embedding — A dense vector representation that captures semantic meaning, mapping discrete items like words into continuous mathematical space.
- Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
- Curse of Dimensionality — Various phenomena that arise when analyzing and organizing data in high-dimensional spaces, becoming sparse and difficult to generalize.