Pls, I need your help. I have a matrix of features X(100*2071 double). Then, I applied svd() on X as in the following code. I read a lot about svd (singular value decompisition) but I can not understand what is the purpose from s as in the code.

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FAS 2018-1-10

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https://ww2.mathworks.cn/matlabcentral/answers/376507-pls-i-need-your-help-i-have-a-matrix-of-features-x-100-2071-double-then-i-applied-svd-on-x-as

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clear; clc;

load X; [ s, ~ ] = svd( X ); D = s( :, 1:20 );%100*20 %%Take only the 20 columns from s

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FAS 2018-1-10

Thank you so much for your reply. So the purpose from that is to reduce the dimension of features. Actually, from my reading about dictionary learning, I found that svd is used to create the dictionaries. Therefore, from the code above, D is a dictionary, which is 100*20 (only the 20 columns from s).

FAS 2018-1-10