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The Eckart-Young Theorem states that the approximation matrix with matrix rank $n$ to a matrix $A$ which has an error matrix with the lowest Frobenius norm is formed by taking the sum of the biggest $n$ elements of the Singular Value Decomposition. This can be used for very, very basic factor analysis.
Todo: explanation.
There is an interesting illustration of low-rank approximation of a matrix, in this case an image, on Staffan Liljegrens Azimuth page.
In the extreme case, given a matrix $A$ where $A_{i j}=f(x_i,y_j)$ for some “tabulated function defined by data”, the Eckart-Young theorem for $n=1$ can be used to find the lowest squared error approximate decomposition into $A^{approx}=\sigma \mathbf{u} \mathbf{v}^T$ (where $|\mathbf{u}|_2=|\mathbf{v}|_2=1$). This corresponds to an approximate decomposition $A_{i j}^{approx}=\sigma f_x(x_i) f_y(y_j)$ for some “tabulated functions” $f_x$ and $f_y$ . This can be analysed to see if a separable function decomposition of $f$ is likely (given most functions $f$ derived from data are contaminated by noise) and used in attempting to fit actual functions to the tables of $f_x$ and $f_y$ values. However this approach only deals with separable cases in two original variables.
“Low Rank Approximation” section of page Singular value decomposition, Wikipedia.
Frobenius norm, Wikipedia.
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