4. Calculate the eigenvalues and eigenvectors of the covariance matrix. Arrange the eigenvalues and eigenvectors in descending order as shown: A=[₁2] S= [S₁ $₂] where ₁ and ₂ are the eigenvalues with the corresponding eigenvectors (column vectors) s₁ and s₂, and λ₁ > 1₂. Note: It is recommended for you to retain the actual values. You may use the STORE function of your calculator for easier accessibility. Note that there will be some discrepancies if rounding off will be done during the solution. The principal components are selected to be the eigenvector/s with the highest eigenvalue/s. The number of eigenvectors to be selected varies and depends on various factors. For this problem, we will only select the highest eigenvalue and the corresponding eigenvector will be the principal component. This is also known as the feature vector.
4. Calculate the eigenvalues and eigenvectors of the covariance matrix. Arrange the eigenvalues and eigenvectors in descending order as shown: A=[₁2] S= [S₁ $₂] where ₁ and ₂ are the eigenvalues with the corresponding eigenvectors (column vectors) s₁ and s₂, and λ₁ > 1₂. Note: It is recommended for you to retain the actual values. You may use the STORE function of your calculator for easier accessibility. Note that there will be some discrepancies if rounding off will be done during the solution. The principal components are selected to be the eigenvector/s with the highest eigenvalue/s. The number of eigenvectors to be selected varies and depends on various factors. For this problem, we will only select the highest eigenvalue and the corresponding eigenvector will be the principal component. This is also known as the feature vector.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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