2. [Prompt 2] Consider a dataset with n points and p=4 features which has a covariance matrix, Ex. The eigenvalues of Ex are given by A₁ = X₂ = 3 and A3 = A4 = 1. The associated eigenvectors are: $1 x1 O || 2 1 [1] 2 Using the top two principal components, which is a 2-dimensional reduction of the following data point: 1,42 = √/2 ° [3] 03 = 04 = √/2 0

Advanced Engineering Mathematics
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ISBN:9780470458365
Author:Erwin Kreyszig
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Chapter2: Second-order Linear Odes
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2. [Prompt 2] Consider a dataset with n points and p = 4 features which has a covariance matrix, Ex. The eigenvalues of Ex are
given by A₁ = ₂ = 3 and X3 = A4 = 1. The associated eigenvectors are:
x1 =
O
1
0
0
O
121
[4]
H
O
° [1]
, 02
7/2
1
, Ф3
Using the top two principal components, which is a 2-dimensional reduction of the following data point:
=
0
,04 = √/2₂
>
0
0
Transcribed Image Text:2. [Prompt 2] Consider a dataset with n points and p = 4 features which has a covariance matrix, Ex. The eigenvalues of Ex are given by A₁ = ₂ = 3 and X3 = A4 = 1. The associated eigenvectors are: x1 = O 1 0 0 O 121 [4] H O ° [1] , 02 7/2 1 , Ф3 Using the top two principal components, which is a 2-dimensional reduction of the following data point: = 0 ,04 = √/2₂ > 0 0
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