 Audrina Jackson

2022-07-08

Benefits of factoring matrix multiplication into two matrix multiplications?
Assuming we have a linear transformation for vectors $\in {\mathbb{R}}^{n}$ to vectors $\in {\mathbb{R}}^{m}$ denoted y=Wx and that the goal is to learn the values of W (or at least the values that produce minimum loss) from data.
My question is as follows:
Is there any benefit from splitting the linear transformation to two matrices and learning the factors instead of learning the original one, i.e., if W=AB, then learn A and B instead of learning W? Are there any mathematical properties of such factoring that make it useful?
Unfortunately, I am unable to find a mathematical term for this matrix factorization (if this is a proper term to use), so it would be great if someone can tell me what should I be reading about.
Also, the only benefit I could find is that if $\mathbf{A}\in {\mathbb{R}}^{m×q},\mathbf{B}\in {\mathbb{R}}^{q×n}$ and q<n, the number of parameters needed would be (n+m)$×$q instead of n$×$m. earendil666h1

Expert

Yes there are lots of pro:s of doing that. Finding good factorizations can lead to more efficient algorithms in natural sciences and engineering. Maybe one of the most famous ones, the FFT ( Fast Fourier Transform ) can indeed be interpreted as a matrix factorization.
The original matrix is dense ( has no values equal to 0, but in the FFT factorization each row of each factor matrix has only a small prime number of non-zero entries, usually 2 non-zero values and there is only ${\mathrm{log}}_{2}\left(N\right)$ such matrix factors.
Now the FFT is factoring more than once and into more than two matrices, but this can be done step-by-step in a systematic fashion where each step splits one matrix into two.

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