T/F Questions. Justify your answer. (a) ) In case of PCA, the rst principal comp
ID: 3761712 • Letter: T
Question
T/F Questions. Justify your answer.
(a) ) In case of PCA, the rst principal component is the direction of maximum variability of
data.
(b) ( Output of a clustering algorithm depends heavily on initial cluster center assignments.
(c) Outliers have no e
ect on k-means clustering results.
(d) In case of hierarchical clustering, the number of clusters required need to be specied
up front.
(e) PCA requires the label information to nd the principal component directions.
(f-K-means clustering allows us to see clusters at di
erent level of granularity/resolution.
(h) Linear dimension reduction corresponds to nding a good projection matrix.
(i) ( PCA is essentially a change of co-ordinate system.
(j) Linear Discriminant Analysis is an unsupervised dimension reduction technique.
(k) Dimension reduction, when viewed as matrix factorization, helps to reveal latent aspects
of data.
(l) Kernel PCA is a supervised learning technique.
Explanation / Answer
a) TRUE because PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called as principal components.This transformation is defined in such a way that the first principal component has the largest possible variance and each succesding component has highest variance possible under constraint that it is orthogonal to preceding componenet.
b) TRUE because the final result depends upon the correctness of intial centroids which are selected randomly.so results in poor minima and secondly it can find only linearly seperable clusters.
c)FALSE because it is a widely popular outlier detection algorithm.so definitly outlier has a effect on the k means algorithm.
d)FALSE because in hierarchical clustering the no of clusters does not have to be specified,instead the hierachical clustering creates a hierachy of clusters.
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