In the last article, we talked about how with the correct mathematical definition, we can define a circle, something that usually lives in two dimensions, in any dimension we want. Hopefully, that exercise has helped make it a bit clearer what we mean when we talk about *high-dimensional data*.

Dimensionality reduction is a technique in which one can put in high-dimensional data (like the example in the last article about films) and run it through an algorithm and try to figure out which dimensions are the most important for understanding similarities and differences between these data points. And these important…

These days, one frequently hears about “high-dimensional” data in the context of machine learning, but it’s rather impossible as a three-dimensional being to imagine anything above three dimensions. Luckily, we can use mathematical definitions to take for example the idea of a circle (usually something that exists in two dimensions) and take it up to three or down to one. In this article, we will play with dimensions a bit.

When we talk about a circle, what we mean is a set of points that are all the same distance away from some middle point. To make our equations nice…

We are the R&D/Analytics department at ZDF Digital. In our day-to-day we identify future & innovation topics in media and offer substantiated analysis services.