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Can anybody explain the main difference between f-test and t-test?,
where i want to use it as method to identify the effective features among a list 60 features extracted out of two types of data (i.e, data 1 composed of 30 image for each image 60 features are extracted/ data 2 composed of 40 images for each image 60 features are extracted), i do search but i still misunderstand the difference..
Note that: the matlab equations for t- test is (h = ttest2(x,y)) and f-test is (H = vartest2(X,Y))
t-test is used to test if two sample have the same mean. The assumptions are that they are samples from normal distribution.
f-test is used to test if two sample have the same variance. Same assumptions hold.
I have little to no experience in image processing to comment on if these tests make sense to your application. A little more info of the problem you are trying to solve will be useful.
If you are however solving a classification problem (categorizing your images) You can use stepwise logistic regression with F-statistics criterion to reduce your predictor dimension:
Alternatively you could use PCA as Image Analyst suggested which does not take into account the response when reducing the dimensionality.