What UMAP (Uniform Manifold Approximation and Projection) Meaning, Applications & Example
Dimensionality reduction technique for visualization.
What is UMAP (Uniform Manifold Approximation and Projection)?
UMAP is a dimensionality reduction technique that is used for visualizing high-dimensional data in lower dimensions (typically 2D or 3D). It works by preserving both local and global structure in data, making it useful for clustering , visualization, and feature learning.
How UMAP Works
- Preserves Local Structure: UMAP focuses on maintaining the local relationships between data points in lower dimensions.
- Global Structure: Unlike some other techniques like t-SNE, UMAP also considers the global structure, meaning that clusters or groups remain distinct even in reduced dimensions.
- Non-linear: It is a non-linear dimensionality reduction technique, meaning it can handle more complex data patterns compared to linear methods like PCA.
Applications of UMAP
- Data Visualization: UMAP can project high-dimensional data into 2D or 3D spaces for easy visualization, often used in clustering and classification tasks.
- Feature Extraction: It is used in pre-processing to reduce the number of features in a dataset, speeding up machine learning algorithms while preserving important patterns.
- Anomaly Detection : By reducing dimensionality, UMAP can help identify outliers in large datasets, as unusual data points may be easier to spot in lower dimensions.
Example of UMAP
In gene expression data analysis, UMAP can be used to reduce the complexity of high-dimensional gene expression data, allowing researchers to visually explore and identify clusters of genes or samples with similar expression profiles.