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Unsupervised Learning
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Dimensionality Reduction
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PCA
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620.
Principal Components Definition
easy
What are the principal components in PCA?
A
The directions of minimum variance in the data that are orthogonal to the decision boundary
B
The cluster centroids found by K-means in the reduced-dimensional feature space
C
The columns of the original feature matrix after standardization and mean-centering
D
The eigenvectors of the covariance matrix of the data, ordered by their eigenvalues
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