Data is normally assumed to exist as a multidimensional cloud, but are we missing something more? Perhaps we need to explore geodesic distance of data - or the space between data.
In this article, Paul Beinat who leads Finity's AI practice area explores the methods that have been used to represent data, data structure and data dimension.
A new algorithm has been developed that follows and measures the geodesic of the manifold of data. Tests are detailed to determine whether this algorithm finds false positives, a geodesic where none exists, and whether it can find a geodesic when artificially prepared data is analysed. Finally, we use the algorithm on a large real world data set.