Dimensionality Reduction

What is dimensionality reduction?

Dimension Reduction refers to the process of converting a set of data having vast dimensions into data with lesser dimensions ensuring that it conveys similar information concisely. These dimensionality reduction techniques are typically used while solving machine learning problems to obtain better features for a classification or regression task.

What are the different dimensionality reduction techniques?

There are multiple dimensionality reduction techniques for machine learning. There are name of some of them:

1. Missing Value Ratio
2. Low Variance Filter
3. High Correlation Filter
4. Random Forest
5. Backward Feature Elimination
6. Forward Feature Selection
7. Factor Analysis
8. Principal Component Analysis (PCA)
9. Independent Component Analysis
10. Methods Based on Projections
11. t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. UMAP