Max Daniels Northeastern Univeristy daniels.g@northeastern.edu |
Catherine Huang University of California, Berkeley thecatherinehuang@berkeley.edu |
Shubham Makharia Brown University shubham@brown.edu |
Chloe Makdad Butler University cmakdad@butler.edu |
Welcome to the project page for our Summer@ICERM 2020 project!
We study the Graph Spectral Clustering algorithm, an alternative to Euclidean clustering algorithms like K-Means, which is geometrically motivated for use on data with few intrinsic factors of variation. It is often challenging to cluster this type of data because one must determine which features are meaningful and which are not. A useful rule of thumb is that in high dimensional settings, only local Euclidean distances are meaningful.
Spectral clustering takes this into account by operating on a similarity graph derived from the data. Edges in the graph represent data similarity and are typically computed with a kernel function like RBF or the exponential kernel. After obtaining the similarity graph, Graph Spectral Clustering utilizes a derived matrix, the Graph Laplacian to find cluster assignments.
In this work, we study the role of the Graph Laplacian and connect Graph Spectral Clustering to the behavior of heat diffusion, which is governed by a second order PDE involving the Laplacian operator. For more information, please see our report!
We thank our project supervisors, Akil Narayan and Minah Oh, as well as our graduate TA supervisors, Alex Mihai and Liu Yang. We also thank our peers at ICERM for useful discussion, and all of the ICERM organizers for a hosting a wonderful program.
The domain is a circle graph with two equidistant low-weight edges. In the first row, we show consecutive eigenvectors of the Graph Laplacian. In the second row, we show how diffusion of random initial conditions converges to eigenvectors of the Graph Laplacian. Different columns show the solution projected onto increasingly high eigenvectors (ie. "high frequencies"). The third row shows energy remaining after this projection.
You can see our work in some of the following Jupyter notebooks:
We explored how graph spectral clustering works.
Since the graph Laplacian is intimately related to the Laplace operator, we decided to look at some partial differential equations that included the Laplace operator. The first equation we investigated was the heat equation.
The next PDE we investigated was the wave equation.
We also applied this algorithm on some real world datasets.