
Separability, SVD and low-rank approximation of 2D image processing filters - hardmaru
https://bartwronski.com/2020/02/03/separate-your-filters-svd-and-low-rank-approximation-of-image-filters/
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jacobolus
Here’s a relevant recent lecture from Alex Townsend, as part of Gil Strang’s
18.065 course, “Rapidly Decreasing Singular Values”
[https://www.youtube.com/watch?v=9BYsNpTCZGg](https://www.youtube.com/watch?v=9BYsNpTCZGg)

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dispanser
Currently watching the lecture series, and it's a great experience. In fact,
this made me understand the blog post to some level I wouldn't have thought
possible only a few weeks ago.

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noelwelsh
Good stuff. A lot of clustering methods can be viewed as matrix factorization
(see, e.g.,
[https://arxiv.org/abs/1512.07548](https://arxiv.org/abs/1512.07548)) so it
would be interesting to see if you could spend more computation on the
creating the low dimensional approximation to achieve a better result. E.g.
perhaps optimizing the absolute error (L1 norm) would be better than the
squared error (L2 norm)?

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id_ris
Very nice writeup. It's fun to see linear algebra out in the wild.

