26,021 research outputs found
Online unit clustering in higher dimensions
We revisit the online Unit Clustering and Unit Covering problems in higher
dimensions: Given a set of points in a metric space, that arrive one by
one, Unit Clustering asks to partition the points into the minimum number of
clusters (subsets) of diameter at most one; while Unit Covering asks to cover
all points by the minimum number of balls of unit radius. In this paper, we
work in using the norm.
We show that the competitive ratio of any online algorithm (deterministic or
randomized) for Unit Clustering must depend on the dimension . We also give
a randomized online algorithm with competitive ratio for Unit
Clustering}of integer points (i.e., points in , , under norm). We show that the competitive ratio of
any deterministic online algorithm for Unit Covering is at least . This
ratio is the best possible, as it can be attained by a simple deterministic
algorithm that assigns points to a predefined set of unit cubes. We complement
these results with some additional lower bounds for related problems in higher
dimensions.Comment: 15 pages, 4 figures. A preliminary version appeared in the
Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA
2017
Template-based searches for gravitational waves: efficient lattice covering of flat parameter spaces
The construction of optimal template banks for matched-filtering searches is
an example of the sphere covering problem. For parameter spaces with
constant-coefficient metrics a (near-) optimal template bank is achieved by the
A_n* lattice, which is the best lattice-covering in dimensions n <= 5, and is
close to the best covering known for dimensions n <= 16. Generally this
provides a substantially more efficient covering than the simpler hyper-cubic
lattice. We present an algorithm for generating lattice template banks for
constant-coefficient metrics and we illustrate its implementation by generating
A_n* template banks in n=2,3,4 dimensions.Comment: 10 pages, submitted to CQG for proceedings of GWDAW1
Learning to Approximate a Bregman Divergence
Bregman divergences generalize measures such as the squared Euclidean
distance and the KL divergence, and arise throughout many areas of machine
learning. In this paper, we focus on the problem of approximating an arbitrary
Bregman divergence from supervision, and we provide a well-principled approach
to analyzing such approximations. We develop a formulation and algorithm for
learning arbitrary Bregman divergences based on approximating their underlying
convex generating function via a piecewise linear function. We provide
theoretical approximation bounds using our parameterization and show that the
generalization error for metric learning using our framework
matches the known generalization error in the strictly less general Mahalanobis
metric learning setting. We further demonstrate empirically that our method
performs well in comparison to existing metric learning methods, particularly
for clustering and ranking problems.Comment: 19 pages, 4 figure
Improved Orientation Sampling for Indexing Diffraction Patterns of Polycrystalline Materials
Orientation mapping is a widely used technique for revealing the
microstructure of a polycrystalline sample. The crystalline orientation at each
point in the sample is determined by analysis of the diffraction pattern, a
process known as pattern indexing. A recent development in pattern indexing is
the use of a brute-force approach, whereby diffraction patterns are simulated
for a large number of crystalline orientations, and compared against the
experimentally observed diffraction pattern in order to determine the most
likely orientation. Whilst this method can robust identify orientations in the
presence of noise, it has very high computational requirements. In this
article, the computational burden is reduced by developing a method for
nearly-optimal sampling of orientations. By using the quaternion representation
of orientations, it is shown that the optimal sampling problem is equivalent to
that of optimally distributing points on a four-dimensional sphere. In doing
so, the number of orientation samples needed to achieve a indexing desired
accuracy is significantly reduced. Orientation sets at a range of sizes are
generated in this way for all Laue groups, and are made available online for
easy use.Comment: 11 pages, 7 figure
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