How to apply PCA to select the best feature(s) from two Frequency Response Functions (FRF)?

Hi, all

I have two FRFs, one for a Fault-free case and the other one for a Faulty case. The bandwidth for each FRF is 2048 Hz and 4096 spectral lines. For Machine Learning purpose, I don't want to use the whole bandwith to compare both FRFs, but instead I want to apply PCA to automatically select the best features that distinguish the Faulty case from the Fault-free one. Any help, please! Thanks in advance.

Answer 1

[Milan Bansal]

Hi Hussein,

As per my understanding, you want to apply Principal Component Analysis (PCA) to your data and select a particular number of features to distinguish the faulty cases from the fault-free cases.

Please refer to the following steps and pseudo code in the code snippet below to apply PCA and select a particular number of best features.

% 1.) Assuming that the given data has 8 features and 1000 entries.  
data = rand(100, 8); % Replace this with your actual data 

% 2.) Normalize the data  
dataNormalized = normalize(data); 

% 3.) Apply PCA 
coeff = pca(dataNormalized); 
% Each column of coeff contains coefficients for one principal component,  
% and the columns are in descending order of component variance. 

% 4.) Assuming that 4 best feature are required out of the given 8. 
numComponents = 4; 
coeff_selected = coeff(:, 1:numComponents); 

% 5.) Project the data onto the selected principal components 
dataProjected = dataNormalized * coeff_selected; 

'dataProjected' is now the given data represented by the four most important features. Please note that after PCA, the features are not the original features but rather the principal components, which are linear combinations of your original features.

Please refer to the following documentation link to learn more about "pca" and "normalize" functions.

• https://www.mathworks.com/help/stats/pca.html 
• https://www.mathworks.com/help/matlab/ref/double.normalize.html 

Hope this helps!

[Hussein]

Milan Bansal

Greatly appreciate your answer. It really helps.