svd
- svd(X) returns singular values vector resulting from singular values decomposition of matrix X
- [U,S,V] = svd(X) returns diagonal matrix S and unitary matrices U and V such that X = U * S * V'
- [U,S,V] = svd(X,0) returns "thin" matrices U and V
Examples
> X = [1 2; 3 4; 5 6]#
1 2
3 4
5 6
> [U S V] = svd(X);disp(U);disp(S);disp(V)
U =
0.229848 -0.883461 0.408248
0.524745 -0.240782 -0.816497
0.819642 0.401896 0.408248
S =
9.52552 0
0 0.514301
0 0
V =
0.619629 0.784894
0.784894 -0.619629
[U:3x3 double]
[S:3x2 double]
[V:2x2 double]
> [U S V] = svd(X,0);disp(U);disp(S);disp(V)
U =
0.229848 -0.883461
0.524745 -0.240782
0.819642 0.401896
S =
9.52552 0
0 0.514301
0 0
V =
0.619629 0.784894
0.784894 -0.619629
[U:3x2 double]
[S:3x2 double]
[V:2x2 double]
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