Hi Hend,
It is my understanding that you have extracted a large number of features from your data using a Gabor filter bank, and now you wish to reduce the dimensionality of these features using Principal Component Analysis (PCA).
To perform PCA in MATLAB, you can use the built-in pca function. Suppose your feature matrix is called features, where each row represents a sample and each column represents a feature. You can apply PCA as follows:
[coeff, score, latent] = pca(features);
In the above example, score gives you the transformed features in the new PCA space.
To reduce the number of features, select the first N columns of score that cumulatively explain your desired amount of variance (e.g., 95%). For more information, you can refer the following pca documentation: https://www.mathworks.com/help/stats/pca.html
Hope this helps!
