how to reduce the dimension of a feature space?

I have 260 sample,each sample has 320 feature (x is a matrix with 260 rows & 320 column).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 !!!!

Answer 1

[David Sanchez]

you may use reshape.

help reshape

[sorena mirzaie]

I check it,but in B=reshape(A),number of elements in A and B should be the same.

actually i want to reduce dimension my feature space according to its principle componants.

[pc,score,latent,tsquare] = princomp(X);

above line give me the principle componant of X,

i want to use just 50 dimensions among 320 dimensions.

red_dim = score(:,1:50);

above line do this for me ,but i dont know how to apply it to matrix X