9 Comments
May 6Liked by Tivadar Danka

Nice post by the way, I’d comment that the axes (the angles) look a little bit strange to represent the 3d plane in some moment I thought wait why 3D vectors if the draw are in 2D space? Then I saw again and looked third dimension.

Expand full comment
May 6Liked by Tivadar Danka

There is a mistake in the plot called final step, it’s the orthogonal projection of a3. The formulas are ok, but not the text

Expand full comment
Apr 19Liked by Tivadar Danka

Thank you for presenting your great work!

I always look at this with the glasses of causality. Let's assume we have 3 features like A (Altitude), T (Temperature) and AQ (Air Quality). We have 10 data points of this 3 variables, where A is the root-cause of T and AD, while T and AQ are purely correlated. How would the Gram-Schmidt process work in this example? Would it reveal the correlation from the causality?

Expand full comment

Nice job! One question I have never asked to you, how do you create such great illustrations?

Expand full comment
Apr 19Liked by Tivadar Danka

Good explanation 👑

Expand full comment