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Unsupervised Learning
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Dimensionality Reduction
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PCA
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589.
PCA Primary Goal
easy
What is the primary goal of Principal Component Analysis?
A
To find orthogonal directions of maximum variance in the data for dimensionality reduction
B
To remove correlated features by projecting data onto an uncorrelated feature subspace
C
To find the directions that maximize class separation for supervised classification tasks based on labeled training data
D
To compress data by encoding each sample as a weighted sum of cluster centroids
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