Clustering Application with a Huge Number of Clusters
In `Learning Feature Representations with K-means' [1] the authors apply unsupervised learning, i.e., k-means to learn 256 features (centroids, centers).
They also claim that the huge number of centers, in this case, k=256 is a limitation to this approach.
p[11]:
``Indeed, it is frequently best to set k as large as compute resources will
allow, considering data requirements. Though performance typically asymptotes
as k becomes extremely large, increasing the size of k is a very effective way
to squeeze out a bit of extra performance from an already-built system''
[1] https://www-cs.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf