Skip to content

DOC: Update tutorial-svd.md fix a typo #208

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jun 3, 2024
Merged

DOC: Update tutorial-svd.md fix a typo #208

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion content/tutorial-svd.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@ $$U \Sigma V^T = A$$

where $U$ and $V^T$ are square and $\Sigma$ is the same size as $A$. $\Sigma$ is a diagonal matrix and contains the [singular values](https://en.wikipedia.org/wiki/Singular_value) of $A$, organized from largest to smallest. These values are always non-negative and can be used as an indicator of the "importance" of some features represented by the matrix $A$.

Let's see how this works in practice with just one matrix first. Note that according to [colorimetry](https://en.wikipedia.org/wiki/Grayscale#Colorimetric_(perceptual_luminance-reserving)_conversion_to_grayscale),
Let's see how this works in practice with just one matrix first. Note that according to [colorimetry](https://en.wikipedia.org/wiki/Grayscale#Colorimetric_(perceptual_luminance-preserving)_conversion_to_grayscale),
it is possible to obtain a fairly reasonable grayscale version of our color image if we apply the formula

$$Y = 0.2126 R + 0.7152 G + 0.0722 B$$
Expand Down
Loading