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I was reading this paper about spectral clustering:

Ng et all. (Nips 2001) http://ai.stanford.edu/~ang/papers/nips01-spectral.pdf

Short question:

What is the connection between the eigenvalues of a Laplacian?

Long question:

I got stuck on why the process makes sense.

Substantially the idea is that some data cannot be simply represented by distribution, but rather as vertices of a graph, where something simpler than graph-cut can be employed.

The "something simpler" is to construct a Laplacian matrix from the graph and then use

the eigenvectors of this matrix as new data and then perform a traditional clustering

algorithm. In this way the data are more "manegeable".

The part where I got stuck is why the eigenvectors are more manegeable?

In a survey about methods for spectral clustering (von Luxburg 2007), when they talk about this paper they say something like: "hence the graph laplacian measures the variation of

a function along a graph: the eingenvalue is low if close points have close function value".

this anyway does not explain why a classification using eigenvectors is easier?

In other words, does the eigenvalues capture the local "shape variation"?

This is what I gather from

http://en.wikipedia.org/wiki/Hearing_the_shape_of_a_drum

and reading other stuff about the eigenvalues of the laplace-beltrami

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# Spectra of the Laplacian (Matrix)

Can you offer guidance or do you also need help?

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