I also doubt that all vectors are Orthogonal and/or Independent.

Re: distance metrics and curvilinear spaces and skew coordinates: https://news.ycombinator.com/item?id=41873650 :

> How does the distance metric vary with feature order?

> Do algorithmic outputs diverge or converge given variance in sequence order of all orthogonal axes? Does it matter which order the dimensions are stated in; is the output sensitive to feature order, but does it converge regardless? [...]

>> Are the [features] described with high-dimensional spaces really all 90° geometrically orthogonal?

> If the features are not statistically independent, I don't think it's likely that they're truly orthogonal; which might not affect the utility of a distance metric that assumes that they are all orthogonal

Which statistical models disclaim that their output is insignificant if used with non-independent features? Naieve Bayes, Linear Regression and Logistic Regression, LDA, PCA, and linear models in general are unreliable with non-independent features.

What are some of the hazards of L1 Lasso and L2 Ridge regularization? What are some of the worst cases with outliers? What does regularization do if applied to non-independent and/or non-orthogonal and/or non-linear data?

Impressive but probably insufficient because [non-orthogonality] cannot be so compressed.

There is also the standing question of whether there can be simultaneous encoding in a fundamental gbit.