Principal Component Analysis (PCA)
A linear dimensionality reduction technique that finds principal components capturing maximum variance.
In more detail
PCA identifies orthogonal directions of maximum variance in data. Projects high-dimensional data onto these components. Unsupervised, interpretable, and computationally efficient. Useful for visualization and preprocessing.
Examples
- Image compression
- Data visualization
- Noise reduction
- Feature extraction
Related terms
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