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how to reduce the dimension of a feature space?
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I have 260 sample,each sample has 320 feature (x is a matrix with 260 rows(images) & 320 column(features)).in order to improve my classification,i need to reduce these 320 column(i mean number of features).but i dont know how to do. when i use for example:
[pc,score,latent,tsquare] = princomp(X); red_dim = score(:,1:50); how to reconstruct the matrix with fewer column?
when i use :
residuals = pcares(X,ndim) the dimension of residuals is the same of x !!!!
actually i dont know what is my new matrix?
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