Principal Component Analysis (PCA)
Description: PCA reduces the dimensionality of data by transforming it to a new set of orthogonal features (principal components) that capture the maximum [[Variance]].
Key Points:
- Reduces complexity of data.
- Helps in visualizing high-dimensional data.
- Assumes linear relationships among features.
Applications: Data compression, noise reduction, feature extraction.