14,330 research outputs found
Set Estimation Under Biconvexity Restrictions
A set in the Euclidean plane is said to be biconvex if, for some angle
, all its sections along straight lines with inclination
angles and are convex sets (i.e, empty sets or
segments). Biconvexity is a natural notion with some useful applications in
optimization theory. It has also be independently used, under the name of
"rectilinear convexity", in computational geometry. We are concerned here with
the problem of asymptotically reconstructing (or estimating) a biconvex set
from a random sample of points drawn on . By analogy with the classical
convex case, one would like to define the "biconvex hull" of the sample points
as a natural estimator for . However, as previously pointed out by several
authors, the notion of "hull" for a given set (understood as the "minimal"
set including and having the required property) has no obvious, useful
translation to the biconvex case. This is in sharp contrast with the well-known
elementary definition of convex hull. Thus, we have selected the most commonly
accepted notion of "biconvex hull" (often called "rectilinear convex hull"): we
first provide additional motivations for this definition, proving some useful
relations with other convexity-related notions. Then, we prove some results
concerning the consistent approximation of a biconvex set and and the
corresponding biconvex hull. An analogous result is also provided for the
boundaries. A method to approximate, from a sample of points on , the
biconvexity angle is also given
Convex Hull of Points Lying on Lines in o(n log n) Time after Preprocessing
Motivated by the desire to cope with data imprecision, we study methods for
taking advantage of preliminary information about point sets in order to speed
up the computation of certain structures associated with them.
In particular, we study the following problem: given a set L of n lines in
the plane, we wish to preprocess L such that later, upon receiving a set P of n
points, each of which lies on a distinct line of L, we can construct the convex
hull of P efficiently. We show that in quadratic time and space it is possible
to construct a data structure on L that enables us to compute the convex hull
of any such point set P in O(n alpha(n) log* n) expected time. If we further
assume that the points are "oblivious" with respect to the data structure, the
running time improves to O(n alpha(n)). The analysis applies almost verbatim
when L is a set of line-segments, and yields similar asymptotic bounds. We
present several extensions, including a trade-off between space and query time
and an output-sensitive algorithm. We also study the "dual problem" where we
show how to efficiently compute the (<= k)-level of n lines in the plane, each
of which lies on a distinct point (given in advance).
We complement our results by Omega(n log n) lower bounds under the algebraic
computation tree model for several related problems, including sorting a set of
points (according to, say, their x-order), each of which lies on a given line
known in advance. Therefore, the convex hull problem under our setting is
easier than sorting, contrary to the "standard" convex hull and sorting
problems, in which the two problems require Theta(n log n) steps in the worst
case (under the algebraic computation tree model).Comment: 26 pages, 5 figures, 1 appendix; a preliminary version appeared at
SoCG 201
A Time-Space Tradeoff for Triangulations of Points in the Plane
In this paper, we consider time-space trade-offs for reporting a triangulation of points in the plane. The goal is to minimize the amount of working space while keeping the total running time small. We present the first multi-pass algorithm on the problem that returns the edges of a triangulation with their adjacency information. This even improves the previously best known random-access algorithm
Archetypal analysis of galaxy spectra
Archetypal analysis represents each individual member of a set of data
vectors as a mixture (a constrained linear combination) of the pure types or
archetypes of the data set. The archetypes are themselves required to be
mixtures of the data vectors. Archetypal analysis may be particularly useful in
analysing data sets comprising galaxy spectra, since each spectrum is,
presumably, a superposition of the emission from the various stellar
populations, nebular emissions and nuclear activity making up that galaxy, and
each of these emission sources corresponds to a potential archetype of the
entire data set. We demonstrate archetypal analysis using sets of composite
synthetic galaxy spectra, showing that the method promises to be an effective
and efficient way to classify spectra. We show that archetypal analysis is
robust in the presence of various types of noise.Comment: 6 pages, 5 figures, 1 style-file. Accepted for publication by MNRA
Random Convex Hulls and Extreme Value Statistics
In this paper we study the statistical properties of convex hulls of
random points in a plane chosen according to a given distribution. The points
may be chosen independently or they may be correlated. After a non-exhaustive
survey of the somewhat sporadic literature and diverse methods used in the
random convex hull problem, we present a unifying approach, based on the notion
of support function of a closed curve and the associated Cauchy's formulae,
that allows us to compute exactly the mean perimeter and the mean area enclosed
by the convex polygon both in case of independent as well as correlated points.
Our method demonstrates a beautiful link between the random convex hull problem
and the subject of extreme value statistics. As an example of correlated
points, we study here in detail the case when the points represent the vertices
of independent random walks. In the continuum time limit this reduces to
independent planar Brownian trajectories for which we compute exactly, for
all , the mean perimeter and the mean area of their global convex hull. Our
results have relevant applications in ecology in estimating the home range of a
herd of animals. Some of these results were announced recently in a short
communication [Phys. Rev. Lett. {\bf 103}, 140602 (2009)].Comment: 61 pages (pedagogical review); invited contribution to the special
issue of J. Stat. Phys. celebrating the 50 years of Yeshiba/Rutgers meeting
Non-homogeneous polygonal Markov fields in the plane: graphical representations and geometry of higher order correlations
We consider polygonal Markov fields originally introduced by Arak and
Surgailis (1989). Our attention is focused on fields with nodes of order two,
which can be regarded as continuum ensembles of non-intersecting contours in
the plane, sharing a number of features with the two-dimensional Ising model.
We introduce non-homogeneous version of polygonal fields in anisotropic
enviroment. For these fields we provide a class of new graphical constructions
and random dynamics. These include a generalised dynamic representation,
generalised and defective disagreement loop dynamics as well as a generalised
contour birth and death dynamics. Next, we use these constructions as tools to
obtain new exact results on the geometry of higher order correlations of
polygonal Markov fields in their consistent regime.Comment: 54 page
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