Hi Joynjo,
To perform Linear Discriminant Analysis (LDA) for dimensionality reduction and visualize the results, you can use MATLAB's fitcdiscr function. LDA is particularly useful for maximizing the separation between multiple classes. Here's how you can project your high-dimensional data onto a lower-dimensional space using LDA and visualize the results:
% Example data
% X is your data matrix (100 observations, 61 features)
% Y is your class labels vector (100 observations)
% Replace these with your actual data
% X = ...;
% Y = ...;
% Fit LDA model
ldaModel = fitcdiscr(X, Y);
% Transform data using LDA
[~, score] = predict(ldaModel, X);
% Plot the first two components
figure;
gscatter(score(:,1), score(:,2), Y);
xlabel('LDA 1');
ylabel('LDA 2');
title('LDA Projection');
legend('show');
grid on;
Here is a documentation link for the same:
