SVD


## Singular Value Decomposition (Full SVD)
$$A = U\Sigma V^T$$
> - U is *ortho-normal and left* singular vector (mxm)
> - eigenvectors of $AA^T$

> - $\Sigma$ is diagonal and singular values (mxn)

> - V is *ortho-normal and right *singular vector (nxn)
> - eigenvectors of $A^TA$
> 

### SVD Notion
|Title|
|:-:|
|[Singular Vector](https://texonom.com/singular-vector-85b671e8460749eab2e61f550beb61d0)|
|[Singular Value](https://texonom.com/singular-value-651478a593d742fab0c0858d36e8b140)|
|[SVD Reconstruction](https://texonom.com/svd-reconstruction-b0bb92edc992406db2d328795ff26335)|
|[Reduced SVD](https://texonom.com/reduced-svd-85f68348ca3646b9a0a6e0d15b9054db)|



> [SVD와 PCA, 그리고 잠재의미분석(LSA) · ratsgo's blog](https://ratsgo.github.io/from%20frequency%20to%20semantics/2017/04/06/pcasvdlsa/)

> [특이값 분해(SVD) - 공돌이의 수학정리노트 (Angelo's Math Notes)](https://angeloyeo.github.io/2019/08/01/SVD.html)




