pca model in matlab

To perform PCA in Matlab, you can use the built-in function pca. Here's an example code snippet that demonstrates how to use this function:

main.m
% Load the data
data = load('example_data.mat');

% Run PCA on the data
[coeff, score, latent, tsquared, explained] = pca(data);

% Plot the explained variance
pareto(explained);

% Choose the number of components to keep
num_components = 2; % For example, we choose to keep the first 2 components

% Transform the data using the principal component coefficients
data_transf = data * coeff(:, 1:num_components);

% Plot the transformed data
scatter(data_transf(:,1), data_transf(:,2));
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In this code, we load some example data (in this case, stored in a .mat file), then run PCA on it using the pca function. The function returns several outputs, including the principal component coefficients (coeff), the transformed data (score), and an array of the variance explained by each principal component (explained). We plot the explained variance using the pareto function, then choose the number of components to keep (in this example, 2). Finally, we transform the data using the principal component coefficients, and plot the transformed data.