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Can you name some feature extraction techniques used for dimensionality reduction?
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Answer / Murari Lal
Feature extraction techniques are used to transform the original high-dimensional data into a lower-dimensional representation while preserving essential information. Some common feature extraction methods for dimensionality reduction include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and Factor Analysis. These techniques aim to find linear combinations of the original features that maximize variance, discriminate between classes, or capture underlying factors.
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