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In understanding machine learning Shai Sharev-Scwartz and Shai Ben-David exercice 23.5.

I would like to use SVD to minimize : $$ \text{argmin}_{W \in \mathbb{R}^{n,d}, U \in \mathbb{R}^{d,n}}{\text{ }\sum_{i=1}^{n} \| x_{i} - UWx_{i} \|_{2}^{2} } = \text{argmin}_{W \in \mathbb{R}^{n,d}, U \in \mathbb{R}^{d,n}}{\text{ } \| X - UWX \|_{F}^{2} } $$

Do you know how to do that or do you have a reference for a proof ?

What did I do ? I'm trying to use low rank theorem $$ \text{arg}\min_{B ; \text{ rank}(B) \le k < \text{rank}(X) } \|X - B\|_{F}^{2} = \sum_{i=1}^{k} \sigma_{i} u_{i} v_{i}^{T} $$ When the SVD of $X$ is $\sum_{i=1}^{r} \sigma_{i} u_{i} v_{i}^{T} $

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Here is a solution if some are interested in : let's $X = (x_{1},...,x_{m}) \in M_{d,m}(\mathbb{R})$ it's SVD is $\sum_{i=1}^{r} \sigma_{i}u_{i}v_{i}^{T}$ with $r$ the rank of $X$ and $u_{i}, v_{i}$ two orthonormal famillies of $\mathbb{R}^{d}$ and $\mathbb{R}^{m}$. We have $$ \sum_{i=1}^{m} \| x_{i} - UWx_{i} \|_{2}^{2} = \| X - UW X \|^{2}_{F} $$ with $F$ the Frobenius norm.

Now we see that the rank of $U$ is $\le n$ and we know that $$ \text{argmin}_{B ; \text{ rank }(B) \le k}{\| X - B \|_{F}^{2} } = \sum_{i=1}^{k} \sigma_{i}u_{i}v_{i}^{T} $$ We can understand the problem is solved if we can find $U$ and $W$ such that $UW = \sum_{i=1}^{n} u_{i}u_{i}^{T}$.

So the solution is $U$ the matrix with columns $u_{1},…,u_{n}$ and $W=U^{T}$.

Noticed you didn't use $v_{i}$.

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