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Asked: 2026-06-03T13:03:57+00:00

I’m performing machine learning on a 25 x 125 image set. After getting the

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I’m performing machine learning on a 25 x 125 image set. After getting the rgb components it becomes 9375 features per example (and I have about 675). I was trying fminunc and fminsearch and I thought that there was something wrong with my method, because it was ‘freezing’, but when I decrease the number of features by a factor of 10, it took a while but worked. How can I minimise the number of features, while maintaining the information relevant in the picture? I tried k-means, but I don’t see how that helps, as I still have the same number of features, just that there are a lot of redundancy.

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1 Answer

  1. Editorial Team

    You’re looking for feature reduction or selection methods. For example see this library:
    http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html
    or see this question
    Feature Selection in MATLAB
    If you google feature selection/reduction matlab will find many relevant articles/tools. Or you could google some commonly used methods like PCA (principal component analysis).

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