Separability, SVD and low-rank approximation of 2D image processing filters(bartwronski.com)

Separability, SVD and low-rank approximation of 2D image processing filters

https://bartwronski.com/2020/02/03/separate-your-filters-svd-and-low-rank-approximation-of-image-filters/

6 comments

Good stuff. A lot of clustering methods can be viewed as matrix factorization (see, e.g., 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)?

id_ris6 anni fa

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

[deleted]6 anni fa

jacobolus6 anni fa

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

dispanser6 anni fa

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.

kiriliponi6 anni fa

Very enjoyable, thanks for sharing