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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