data matrix feature mapping in matlab

To perform data matrix feature mapping in MATLAB, you can follow these steps:

1. Load your data matrix into MATLAB.

main.m
data = csvread('data.csv');
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2. Calculate the mean and standard deviation for each feature/column of the data matrix.

main.m
meanVals = mean(data);
stdVals = std(data);
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3. Normalize the data matrix by subtracting mean values and dividing by standard deviations for each column.

main.m
data_norm = (data-meanVals)./stdVals;
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4. Calculate the covariance matrix of the normalized data matrix.

main.m
covariance = cov(data_norm);
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5. Calculate the eigenvectors and eigenvalues of the covariance matrix.

main.m
[eigenvectors, eigenvalues] = eig(covariance);
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6. Sort the eigenvectors and eigenvalues in descending order of their corresponding eigenvalues.

main.m
eigenvalues = diag(eigenvalues);
[~,idx] = sort(eigenvalues,'descend');
eigenvalues = eigenvalues(idx);
eigenvectors = eigenvectors(:,idx);
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7. Choose the top n eigenvectors with largest eigenvalues to create the feature mapping matrix.

main.m
% Choose the top 2 eigenvectors
n = 2; 
featureMapping = eigenvectors(:,1:n);
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Once you have created the feature mapping matrix, you can use it to reduce the dimensionality of your data matrix by multiplying it with the normalized data matrix.

main.m
% Reduce the dimensionality of data matrix
data_reduced = data_norm * featureMapping;
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Note: This example assumes that your data matrix is stored in a comma-separated value (CSV) file named 'data.csv'. You can adjust the code accordingly if your data is stored in a different format.