___________ is a popular method for dimensionality reduction that transforms the data into a new coordinate system where the variance is maximized.
- Feature Selection
- Linear Discriminant Analysis
- Principal Component Analysis
- t-SNE
Principal Component Analysis (PCA) is a method that transforms data into a new coordinate system where the variance is maximized. It's a popular technique for reducing dimensions while preserving as much information as possible in the reduced space.
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