Dimensionality Reduction
The process of reducing the number of random variables under consideration in a dataset.
Description
Dimensionality reduction is the process of reducing the number of features in a dataset while retaining as much information as possible. It's used to simplify datasets, reduce computational complexity, and mitigate the curse of dimensionality.
Examples
- 🧮 Principal Component Analysis (PCA)
- 🗺️ t-SNE
- 🧠 Autoencoders
Applications
🗜️ Data compression
🧹 Noise reduction
📊 Visualization of high-dimensional data
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