I don't like you, and I like them, but maybe if we change, we can get along.Spectral clustering is not clustering: it's actually a metric modification method. It's another way of capturing things that are close versus things that far, using a more global method.

We saw how correlation clustering could be used to capture both the ``I don't like you'' and ``I like them'' aspects of clustering by assigning positive and negative weights to individual element pairs. However, it's not easy to come by such a labelling of pairs. More often than not, all we have is a distance function. We could threshold it, declaring all distances within some parameter r to be positive, and all distances larger to be negative, but this is completely arbitrary and depends on the choice of r.

Let's think of the two-class problem for now: we merely want to divide the points into two clusters. Suppose we were able to guess a function that assigns one label (0) to points in one cluster and another label (1) to points in the other cluster. Then we could write down the cost of the clustering (in a correlation sense) by summing up, over all pairs, the difference

. In fact, just to make sure it's always positive, we'll square it, and thus will sum up the function . We'll also assume a weight function on the edge , so that what we're really summing up is .This is where things start to get very interesting. It's not hard to see that this can be written in term of a variant of the graph Laplacian, treating the f values as a vector. If we now allow the f values to be reals, rather than 0 or 1, we get a classical eigenvalue problem on the Laplacian, in effect determining the second smallest eigenvalue of the Laplacian.

Once we do that, the corresponding eigenvector gives us an assignment for f. we can either merely take positive or negative values of f to group the data, or *we can use the f values as a feature representation* and recluster the data into two groups based on that.

This is the key insight of spectral ``clustering''. We perform a spectral decomposition of the Laplacian, and take the top k eigenvectors. Those vectors give us a k-dimensional feature represntation of the data in an inner product space, and we can now recluster. The real question is: why should this feature representation help us in any way at all ?

Here's my take on what's really going on. We know that the graph Laplacian is a discrete equivalent of the Laplace-Beltrami operator on a manifold, which in turn (via the heat equation) captures the ``flow'' of material in a manifold. Return for a second to our cut-based view of the distance function. In essence, two points are close if there are many ways of getting from one to the other (i.e the min cut between them is large) and they are far if not. This is a flow-based way of thinking about distances, and in effect, what the Laplacian does is map the original data into a new metric space in which distance is based on flow, rather than just on metric distance.

This intuition can be formalized: if we switch now to the stochastic viewpoint, in which we're doing random walks on the graph, then the commute time between two vertices turns out to be precisely the Euclidean distance (squared: thanks, dsivakumar) between the feature vectors constructed from the eigenvalues of the Laplacian.

... take a deep breath...

There are a number of different concepts swirling around here that are worth keeping track of. The cut-based viewpoint of node similarity in graphs lends itself naturally to a Laplacian-based treatment, which in turn leads us to random walks and the heat equation on manifolds. At a deeper level, the Laplacian of a graph, by virtue of how it acts on functions, tells us something very basic about the structure of the distances involved. For more on this particular aspect, you should check out the discussion 'Can you hear the shape of a drum'.

But it's very important to keep in mind that spectral clustering is not so much a clustering technique as a data transformation method. Although k-means is the clustering algorithm of choice once we get down to the feature representation, other methods could be used as well. Also, as relaxations go, this paricular one (relaxing from the hypercube to the reals), isn't that great: the ``integrality gap'' can be quite high. It turns out that this doesn't matter terribly though.

As an aside, the idea of using the graph Laplacian to define a ``stretched'' distance that looks Euclidean is not limited to this problem. The most "famous" version of this idea is the Laplacian eigenmaps work by Belkin and Niyogi in the realm of manifold learning. There's also work by Guibas, Ovsjanikov and Sun that uses similar ideas to create signatures of shapes. The key idea, once again, is that if you're on a manifold of some kind, the Laplacian gives you a handle on the shape of this manifold, as well as how to ``stretch it out'' with local coordinates to make it look flat (and therefore Euclidean). It's a useful trick to keep in your data analysis toolbox.

For further reading, Ulrich von Luxburg has a great survey on spectral clustering.