What's the truncation error in SVD?

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Xiaohan Du
Xiaohan Du 2017-8-30
编辑： Christine Tobler 2017-8-30
Hi all,
I'm trying to prove one thing: perform SVD on matrix a, the truncation error of singular values equals to truncation error of products of singular vectors. Here is the code:
clear; clc;
% PART 1.
% define the matrix a.
a = magic(8);
% define number of singular values left after truncation.
n = 2;
[u, s, v] = svd(a, 0);
% sum all singular values.
ds = diag(s);
dss = sum(ds);
% sum first n singular values
dssn = sum(ds(1:n));
% work out the error after truncation.
dsp = 1 - dssn / dss;
% PART 2
% multiply left singular vectors with singular values
u = u * s;
% select the first n singular vectors, i.e. to truncate
us = u(:, 1:n);
vs = v(:, 1:n);
% reconstruct the solution
ur = us * vs';
% work out the error
up = norm(ur - a, 'fro') / norm(a, 'fro');
I expect dsp = up, but dsp = 0.0431, up = 0.0613, why?

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回答（1 个）

Christine Tobler
Christine Tobler 2017-8-30
编辑：Christine Tobler 2017-8-30
Use dsp = norm(ds(n+1:end)) / norm(ds) instead of the sum.
This is because in norm(U*S*V', 'fro'), you can move U and V outside of the norm since they are orthogonal, leaving you with norm(S, 'fro'), and since S is diagonal, this is equal to norm(diag(S)).

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