So this is absolutely not my area of expertise. Still, I wondered if someone could point me in the direction of a computational distance measure between points in a projective space? (ie. an algorithm I could implement to find distance between two points in projective space).
I am trying to find a distance measure for objects which should be scale invariant and can be embedded as points in $\mathbb{R}^n$, naturally, for scale invariance I thought of 1d subspaces and projective spaces. I know the Grassmanian over a real vector space and thus real projective spaces are smooth manifolds and not necessarily endowed with a metric, so this may bear no fruit. But I have seen some mention of metrics on Grassmannians while trying to understand the literature myself.
Any links to papers (preferable readable by a non-specialist) are much appreciated.
Well, the representation of points in a projective space is not unique. But you can always find a canonical representative of a point, namely $(0,\ldots,0,1,p_1,\ldots,p_k)$, where $p_1,\ldots,p_k$ are arbitrary. From here you could use the Euclidean distance.