This assignment developed learning on: variance-covariance matrix, diagonalizing a matrix, eigenvectors and eigen values, and the practical application of how eigen values can be used to identify principal components (features that contribute the most to the identification of a data analytics problem). More specifically, how Principal Component Analysis (PCA) can be used to reduce the dimensions of data in a data analytics problem.
The assignment is to write a program that includes all the functionality described in four pans shown in Figure 1 below.
Step A is to generate two different samples, from a normal distribution, where the sample size is two for the first sample, and 100 for the second sample. The Mean of those two samples is given as a vector and the covariance matrix is given as a matrix.
Step B plots the generated samples.
“Teislatilif normal distribution specified by N N.100 31,;,…1
E l4 :1.
IX Let io.!s pis,..nd +0r1 0.53,4, IMmonalize these Mo matrices.
suiaciattseinatrixxesosglixd. Figure 1 Assignment 1 Step C computes the sample mean and the sample covariance matrix. Step D provides two matrices and asks that the matrices be diagonalized.
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