(T/F) Questions. (a) (2 points) PCA and Spectral Clustering perform eigen- decom

ID: 3764426 • Letter: #

Question

(T/F) Questions.

(a) (2 points) PCA and Spectral Clustering perform eigen- decomposition on two dierent matrices.

However, the size of these two matrices are the same.

(b) (2 points) Final solution of EM depends heavily on initialization.

(d) (2 points) kernel PCA is a non-linear dimension reduction technique.

(e) (2 points) In k-means algorithm k represents the number of iterations.

(f) (2 points) Data clustering is a supervised learning method.

(g) (2 points) K means clustering allows us to build a dendogram

(h) (2 points) EM algorithm can be used to learn a Gaussian mixture model.

(i) (2 points) Dimension reduction using PCA corresponds to change of basis.

(j) (2 points) Hierarchical clustering can be performed only in top down manner

a) T

b) T

c) F

d) T

e) F

f) F

g)T

h) T

i) T

j) F

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