Vectorize K-means

Jan 8, 2017 • k-means • 2

Problem

Let $X \in \mathbb{R}^{d \times n}$ be a data matrix with examples in its columns (e.g. there are $n$ examples with $d$ features each). Let $C \in \mathbb{R}^{d \times k}$ be a matrix of k-means centroids (cluster centers).

Recall the k-means algorithm iteratively computes two high-level steps:

Find the distances from every example to every centroid.
Update each centroid to be the mean of the examples assigned to it. The examples are assigned to the centroid which they are closest to.

Write a single line vectorized implementation of step 1.

Solution

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We compute the distances from the $i$ -th example to the $j$ -th centroid in the $(i, j)$ -th element of a matrix $D \in \mathbb{R}^{n \times k}$ . Note also that the below is not computing the distance exactly, but the relative distances (all that we need for k-means) are preserved. The exact distances could be computed with a third term similar to the second but with the matrix $X$ . However this is unecessary work.

$\begin{align} D = X^T K + {\bf 1} {\bf 1}^T (K \odot K) \end{align}$

In the above $\odot$ is the Hadamard (elementwise) product and the ${\bf 1}$ vectors have the size necessary for the syntax of the above statement to be sensical (namely $n$ and $d$ respecitvly).

The line could be implemented in code with something like (using numpy syntax)

np.dot(X.T, K) + np.sum(K**2, axis=1, keepdims=True)