E-means: Outperforming k-means in one-tenth the epochs
https://e-means.github.io/Edit: tried it, and it’s definitely not better for this use case. e-means wins by needing fewer passes over a huge dataset, but relaxing a mesh on a sphere has no dataset: each Voronoi cell’s centroid can be computed directly from its geometry. Mini-batches just swap that for a noisy sampled estimate of the same thing. It has the same objective and the same optimum, so at best it matches Lloyd. In practice it was much slower and left the grid noisy, and with small sample budgets it even tangled the mesh.
[1] https://webgcm.app/explain/grid#nudging-the-cells-into-shape
Edit: in fact you might end up going faster by moving the other way, set alpha = -0.1 and see what happens!
That's impressive!
If you are claiming that your algorithm is "highly parallelizable" and comes with "wall-clock speedups", then the actual metrics that matter here is not number of steps or even basic wall clock time as your size remains fixed, but how your algorithm scales as more data is present or parallelism is required. This algorithm splits up the minibatch across workers and has them compute local updates, then allreduces it. As the amount of data goes up, the comms become much more expensive (since you have to use a worse transport layer), which means that you're basically locked in a desperate attempt to do more local work before you send your partial update to everyone else. If you attempt this (not explored in the paper) you will find that the improvement in clustering by doing this extra work has diminishing returns–you only reduce the number of updates by a few steps (which is what drives your wall clock time). At scale you end up being heavily comms limited and not super parallel at all.
Though I suppose it may be as simple as expectation maximization with a friction term, since Sj = sj - a (sj - Sj).
Importantly they note that this update rule keeps the centroid within the convex hull, which is pretty important.
There's still not one particularly great way to pick k for this type of clustering. There kind of can't be as clustering is sort of inherently subjective.
Other methods like HDBSCAN can find the number of clusters themselves. There's still a 'one key parameter' you have to pick yourself, but it can be more intuitive and less sensitive for some datasets, when you don't know how many clusters there are / how many you want.
Sometimes, picking k exactly is useful for practical reasons too. Like fixing the size of your codebook. Or partitioning a dataset into k equal-ish pieces, say to spread across a pool of servers of a known/fixed size.
When I was just starting out, client-server was still a bit of a specialty, mostly reserved for gaming and large institutions. Most of my education was in single task computing. If the university hadn’t offered a class in distributed computing I wouldn’t have gotten much in that direction except in independent study.
In the time since then, we seem to have focused much less on seconds per task and more on tasks per second, which results in more concern about statistical spreads, and queuing mechanisms to manage the variability between a hit and a miss. It would be better if this sort of computation were more organized into a discipline.
You want to assign things to a set of clusters so that things that share similar properties are grouped closely together. But, we also want those clusters to be as big as possible. We don't want to leave little islands that only have a few things in them.
Take, for example, classification of email messages. Let's say you have a billion email messages and you want to find a way to cluster them automatically into 10 categories. You don't know what the categories are ahead of time. You just have a way of making sure that an email can be represented by a vector of numbers such that two emails that are close together in their meaning and characteristics will have similar numbers.
K-Means clustering solves that problem but is intractable to calculate for large datasets having high dimensionality (such as the email example). My understanding is that E-Means is away to achieve this spectacularly more efficiently than the best alternative previously known.
Impressive results ...