

Choosing a vector difference function - abscondment
http://threebrothers.org/brendan/blog/vector-difference-functions/

======
cammil
I would advise those reading too much into this article not do so. Using
vectors for theory to do basic statistics is likely to confuse matters for
you. Vectors, though important in statistics, are primarily used to describe
spaces, be they 3d or otherwise. If you understand how this translates to
statistics, then you probably don't need the ideas in the article. If you
don't, then this article is plainly misleading.

------
vomjom
There are far better methods than the one linked in the article.

You can train a covariance matrix such that you can get a better distance
metric. Particularly, you would use the Mahalanobis Distance:

<http://en.wikipedia.org/wiki/Mahalanobis_distance>

For classification tasks, there are two good ways of training a covariance
matrix for distance metrics: neighborhood components analysis and large margin
nearest neighbors.

The effect in the article is just a particular quirk of using the euclidean
distance. You could, for example, get the same result by using a 1-norm
distance.

------
profquail
This is usually called a "Norm" (or "Vector Norm").

<http://en.wikipedia.org/wiki/Norm_%28mathematics%29>

