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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.

Algebra & Trigonometry with Analytic Geometry
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter9: Systems Of Equations And Inequalities
Section9.8: Determinants
Problem 31E
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4. Calculate the eigenvalues and eigenvectors of the covariance matrix. Arrange the eigenvalues and eigenvectors in descending order as shown: A = . s= A = S = [S1 S2] where 1, and 12 are the eigenvalues with the corresponding eigenvectors (column vectors) s, and s2, and 1, > d2. 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.
Transcribed Image Text:4. Calculate the eigenvalues and eigenvectors of the covariance matrix. Arrange the eigenvalues and eigenvectors in descending order as shown: A = . s= A = S = [S1 S2] where 1, and 12 are the eigenvalues with the corresponding eigenvectors (column vectors) s, and s2, and 1, > d2. 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.
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