# How to choose reduced features using SVD from a dataset?

How svd reduces features from a matrix. suppose, Matrix A(m,n). if we apply svd to A then we will find matrix U(m,m), S(m,n), v(n,n) matrix. S is the strength/ diagonal matrix as I know. We can ignore lesser vales of it. but I don't understand how to choose K features from matrix A to reduce its dimension. can you help to find out it, please? Example:

A = [1 1 1 0 0;
3 3 3 0 0;
4 4 4 0 0;
5 5 5 0 0;
0 2 0 4 4;
0 0 0 5 5;
0 1 0 2 2]
[u,s,v] = svd(A)


now how I find/choose the reduced feature from matrix A?