CS 451 Quiz 20
Principal Component Analysis (PCA)
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Email address
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What are typical applications of PCA?
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Check all that apply.
Line fitting
Clustering
Data compression
Data visualization
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PCA allows us to identify and discard redundant features [this was poorly phrased, sorry!]
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True
False
PCA projects pdimensional data onto a qdimensional subspace, where q < p. What is p?
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the number of data points
the number of features
the number of centroids
PCA computes the same thing as
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linear regression
logistic regression
neither
PCA finds a lowerdimensional subspace minimizing
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the maximum distance between a data point and its projection
the variance of the projections of the data points
the sum of the squared distances between the data points and their projections
In linear regression, one of the coordinate axes is "special", while PCA treats all axes equally
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True
False
What preprocessing is necessary before running PCA?
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Both mean normalization and feature scaling are necessary
Mean normalization is necessary; feature scaling is optional
Mean normalization is optional; feature scaling is necessary
Both mean normalization and feature scaling are optional
The PCA algorithm computes the eigenvectors of Sigma, where Sigma is the
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covariance matrix
subspace descriptor
row decomposition
PCA can be computed via the Matlab command [U, S, V] = svd(Sigma). The resulting kdimensional subspace is spanned by
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the pseudoinverse of S
the first k columns of U
the first k rows of V
What does SVD stand for?
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Software Version Descriptor
Structural Valve Deterioration
Singular Vector Differentiation
Special Value Division
Single Vision Distance
Strategic Vertical Differentiation
Singular Value Decomposition
Signature Verification Data
Strategic Vision Development
Feedback on working in teams
(not part of your quiz grade)
When working on homework problems, which do you prefer?
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Working alone
Working in teams
If teams are mandatory on a homework problem, which do you prefer?
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Choosing team members myself
Teams assigned by the professor
Either is fine
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