CS 451 Quiz 20
Principal Component Analysis (PCA)
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What are typical applications of PCA? *
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PCA allows us to discard redundant features *
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PCA projects p-dimensional data onto a q-dimensional subspace, where q < p. What is p? *
1 point
PCA computes the same thing as *
1 point
PCA finds a lower-dimensional subspace minimizing *
1 point
In linear regression, one of the coordinate axes is "special", while PCA treats all axes equally *
1 point
What preprocessing is necessary before running PCA? *
1 point
The PCA algorithm computes the eigenvectors of Sigma, where Sigma is the *
1 point
PCA can be computed via the Matlab command [U, S, V] = svd(Sigma). The resulting k-dimensional subspace is spanned by *
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What does SVD stand for? *
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When working on homework problems, which do you prefer? *
If teams are mandatory on a homework problem, which do you prefer? *
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