What Dimensionality Reduction Meaning, Applications & Example
Techniques to reduce the number of input variables in a dataset.
What is Dimensionality Reduction?
Dimensionality Reduction is the process of reducing the number of variables or features in a dataset while retaining as much relevant information as possible. This technique simplifies complex datasets, making them easier to analyze and visualize.
Types of Dimensionality Reduction
- Principal Component Analysis (PCA) : Transforms data into a set of uncorrelated components, capturing the most variance with fewer features.
- Linear Discriminant Analysis (LDA): Finds linear combinations of features that best separate classes, used primarily in classification tasks.
- t-SNE (t-Distributed Stochastic Neighbor Embedding) : Maps high-dimensional data to two or three dimensions for visualization, preserving local structure.
Applications of Dimensionality Reduction
- Data Visualization: Enables visualization of high-dimensional data in two or three dimensions, revealing patterns and clusters.
- Noise Reduction : Eliminates less relevant features, improving model performance by focusing on the most informative variables.
- Speed Optimization: Reduces the computational load for machine learning models, making training and inference faster.
Example of Dimensionality Reduction
In image processing, Dimensionality Reduction techniques like PCA help compress image data, removing redundant information while preserving essential features for tasks like image recognition .