1,297 research outputs found
Squarepants in a Tree: Sum of Subtree Clustering and Hyperbolic Pants Decomposition
We provide efficient constant factor approximation algorithms for the
problems of finding a hierarchical clustering of a point set in any metric
space, minimizing the sum of minimimum spanning tree lengths within each
cluster, and in the hyperbolic or Euclidean planes, minimizing the sum of
cluster perimeters. Our algorithms for the hyperbolic and Euclidean planes can
also be used to provide a pants decomposition, that is, a set of disjoint
simple closed curves partitioning the plane minus the input points into subsets
with exactly three boundary components, with approximately minimum total
length. In the Euclidean case, these curves are squares; in the hyperbolic
case, they combine our Euclidean square pants decomposition with our tree
clustering method for general metric spaces.Comment: 22 pages, 14 figures. This version replaces the proof of what is now
Lemma 5.2, as the previous proof was erroneou
Augmenting Graphs to Minimize the Radius
We study the problem of augmenting a metric graph by adding k edges while minimizing the radius of the augmented graph. We give a simple 3-approximation algorithm and show that there is no polynomial-time (5/3-?)-approximation algorithm, for any ? > 0, unless P = NP.
We also give two exact algorithms for the special case when the input graph is a tree, one of which is generalized to handle metric graphs with bounded treewidth
Average Sensitivity of Graph Algorithms
In modern applications of graphs algorithms, where the graphs of interest are
large and dynamic, it is unrealistic to assume that an input representation
contains the full information of a graph being studied. Hence, it is desirable
to use algorithms that, even when only a (large) subgraph is available, output
solutions that are close to the solutions output when the whole graph is
available. We formalize this idea by introducing the notion of average
sensitivity of graph algorithms, which is the average earth mover's distance
between the output distributions of an algorithm on a graph and its subgraph
obtained by removing an edge, where the average is over the edges removed and
the distance between two outputs is the Hamming distance.
In this work, we initiate a systematic study of average sensitivity. After
deriving basic properties of average sensitivity such as composition, we
provide efficient approximation algorithms with low average sensitivities for
concrete graph problems, including the minimum spanning forest problem, the
global minimum cut problem, the minimum - cut problem, and the maximum
matching problem. In addition, we prove that the average sensitivity of our
global minimum cut algorithm is almost optimal, by showing a nearly matching
lower bound. We also show that every algorithm for the 2-coloring problem has
average sensitivity linear in the number of vertices. One of the main ideas
involved in designing our algorithms with low average sensitivity is the
following fact; if the presence of a vertex or an edge in the solution output
by an algorithm can be decided locally, then the algorithm has a low average
sensitivity, allowing us to reuse the analyses of known sublinear-time
algorithms and local computation algorithms (LCAs). Using this connection, we
show that every LCA for 2-coloring has linear query complexity, thereby
answering an open question.Comment: 39 pages, 1 figur
Geodesic tractography segmentation for directional medical image analysis
Acknowledgements page removed per author's request, 01/06/2014.Geodesic Tractography Segmentation is the two component approach presented in this thesis for the analysis of imagery in oriented domains, with emphasis on the application to diffusion-weighted magnetic resonance imagery (DW-MRI). The computeraided analysis of DW-MRI data presents a new set of problems and opportunities for the application of mathematical and computer vision techniques. The goal is to develop a set of tools that enable clinicians to better understand DW-MRI data and ultimately shed new light on biological processes.
This thesis presents a few techniques and tools which may be used to automatically find and segment major neural fiber bundles from DW-MRI data. For each technique, we provide a brief overview of the advantages and limitations of our approach relative to other available approaches.Ph.D.Committee Chair: Tannenbaum, Allen; Committee Member: Barnes, Christopher F.; Committee Member: Niethammer, Marc; Committee Member: Shamma, Jeff; Committee Member: Vela, Patrici
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