MATLAB: What is a good Dimensionality Reduction technique I can use

dimensionality reductiongait recognitionMATLABstepwisefit

I'm currently analyzing human gait, and designing a system for automatic recognition based on those unique traits. My features are extracted by accumulating the difference between sequential frames taken from video sequences of walking subjects. and then applying DCT at one stage to get one feature vector per sample, of dimension 100. I'm using a linear classifier. Upon testing the classifier, I get a Recognition Rate of around 75%. One of the techniques I'm trying to enhance my Rate is Dimensionality Reduction. I'v tried one Pattern-Recognition Tool Box I found on the web, including techniques like PCA, LPP, etc. I've also tried the Matlab function stepwisefit. However, none of the mentioned seems to work with me.

I'd appreciate if anyone can advise me for a good technique I should try. whether a built-in Matlab function or another code? What would be the best way to test these techniques?

Best Answer

Your best chance would be to set up variable selection based on that linear classifier you are using (you don't say what it is). For 100 features, sequentialfs from Statistics Tlbx could produce results within reasonable time; it depends, of course, on how many observations you have. If your data has two classes, I am surprised stepwisefit did not help since linear regression often gives a decent approximation to linear binary classification. If you have R2012a or later, you could try ClassificationDiscriminant with thresholding and possibly regularization. It is also possible that 75% is as good as you can ever do with a linear technique.

Other possibilities are mentioned here: http://www.mathworks.com/matlabcentral/answers/33808-select-machine-learning-features-in-matlab

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