Thread: [Numpy-discussion] Truncated Singular value decomposition

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numpy-discussion

[Numpy-discussion] Truncated Singular value decomposition

From: Nils W. <nw...@me...> - 2002-01-28 14:47:27

Hi all,

how can I obtain a truncated SVD (dyadic decomposition) of

V,S,WT = singular_value_decomposition(Z) ?

I am interested in a low rank approximation of Z.

Thanks in advance.

                      Nils

Re: [Numpy-discussion] Truncated Singular value decomposition

From: Konrad H. <hi...@cn...> - 2002-01-28 16:21:35

Nils Wagner <nw...@me...> writes:

> V,S,WT = singular_value_decomposition(Z) ?
> 
> I am interested in a low rank approximation of Z.

The singular values in S are sorted by decreasing order of magnitude,
so you just replace as many "small" ones by zero as you wish, and
multiply the three matrices back together.

Konrad.
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