  This paper discusses the topic of dimensionality reduction for $k$-means
clustering. We prove that any set of $n$ points in $d$ dimensions (rows in a
matrix $A \in \RR^{n \times d}$) can be projected into $t = \Omega(k / \eps^2)$
dimensions, for any $\eps \in (0,1/3)$, in $O(n d \lceil \eps^{-2} k/ \log(d)
\rceil )$ time, such that with constant probability the optimal $k$-partition
of the point set is preserved within a factor of $2+\eps$. The projection is
done by post-multiplying $A$ with a $d \times t$ random matrix $R$ having
entries $+1/\sqrt{t}$ or $-1/\sqrt{t}$ with equal probability. A numerical
implementation of our technique and experiments on a large face images dataset
verify the speed and the accuracy of our theoretical results.

Artificial Intelligence;Data Structures and Algorithms