Dimensionality Reduction
/dɪˌmɛnʃəˈnælɪti rɪˈdʌkʃən/
Techniques to reduce the number of features in data while preserving important information.
In italiano: Riduzione DimensionalitàDimensionality reduction simplifies data by projecting it to lower dimensions. Benefits include faster computation, reduced storage, and better visualization. Common methods: PCA, t-SNE, UMAP.
Examples
- PCA for visualization
- Feature compression
- Noise reduction