24 research outputs found
Spectrally approximating large graphs with smaller graphs
How does coarsening affect the spectrum of a general graph? We provide
conditions such that the principal eigenvalues and eigenspaces of a coarsened
and original graph Laplacian matrices are close. The achieved approximation is
shown to depend on standard graph-theoretic properties, such as the degree and
eigenvalue distributions, as well as on the ratio between the coarsened and
actual graph sizes. Our results carry implications for learning methods that
utilize coarsening. For the particular case of spectral clustering, they imply
that coarse eigenvectors can be used to derive good quality assignments even
without refinement---this phenomenon was previously observed, but lacked formal
justification.Comment: 22 pages, 10 figure
How to Round Subspaces: A New Spectral Clustering Algorithm
A basic problem in spectral clustering is the following. If a solution
obtained from the spectral relaxation is close to an integral solution, is it
possible to find this integral solution even though they might be in completely
different basis? In this paper, we propose a new spectral clustering algorithm.
It can recover a -partition such that the subspace corresponding to the span
of its indicator vectors is close to the original subspace in
spectral norm with being the minimum possible ( always).
Moreover our algorithm does not impose any restriction on the cluster sizes.
Previously, no algorithm was known which could find a -partition closer than
.
We present two applications for our algorithm. First one finds a disjoint
union of bounded degree expanders which approximate a given graph in spectral
norm. The second one is for approximating the sparsest -partition in a graph
where each cluster have expansion at most provided where is the eigenvalue of
Laplacian matrix. This significantly improves upon the previous algorithms,
which required .Comment: Appeared in SODA 201
Efficient Bounds and Estimates for Canonical Angles in Randomized Subspace Approximations
Randomized subspace approximation with "matrix sketching" is an effective
approach for constructing approximate partial singular value decompositions
(SVDs) of large matrices. The performance of such techniques has been
extensively analyzed, and very precise estimates on the distribution of the
residual errors have been derived. However, our understanding of the accuracy
of the computed singular vectors (measured in terms of the canonical angles
between the spaces spanned by the exact and the computed singular vectors,
respectively) remains relatively limited. In this work, we present bounds and
estimates for canonical angles of randomized subspace approximation that can be
computed efficiently either a priori or a posterior. Under moderate
oversampling in the randomized SVD, our prior probabilistic bounds are
asymptotically tight and can be computed efficiently, while bringing a clear
insight into the balance between oversampling and power iterations given a
fixed budget on the number of matrix-vector multiplications. The numerical
experiments demonstrate the empirical effectiveness of these canonical angle
bounds and estimates on different matrices under various algorithmic choices
for the randomized SVD
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE