70 research outputs found
On the Separability of Stochastic Geometric Objects, with Applications
In this paper, we study the linear separability problem for stochastic geometric objects under the well-known unipoint/multipoint uncertainty models. Let S=S_R U S_B be a given set of stochastic bichromatic points, and define n = min{|S_R|, |S_B|} and N = max{|S_R|, |S_B|}. We show that the separable-probability (SP) of S can be computed in O(nN^{d-1}) time for d >= 3 and O(min{nN log N, N^2}) time for d=2, while the expected separation-margin (ESM) of S can be computed in O(nN^d) time for d >= 2. In addition, we give an Omega(nN^{d-1}) witness-based lower bound for computing SP, which implies the optimality of our algorithm among all those in this category. Also, a hardness result for computing ESM is given to show the difficulty of further improving our algorithm. As an extension, we generalize the same problems from points to general geometric objects, i.e., polytopes and/or balls, and extend our algorithms to solve the generalized SP and ESM problems in O(nN^d) and O(nN^{d+1}) time, respectively. Finally, we present some applications of our algorithms to stochastic convex-hull related problems
Separating bichromatic point sets in the plane by restricted orientation convex hulls
The version of record is available online at: http://dx.doi.org/10.1007/s10898-022-01238-9We explore the separability of point sets in the plane by a restricted-orientation convex hull, which is an orientation-dependent, possibly disconnected, and non-convex enclosing shape that generalizes the convex hull. Let R and B be two disjoint sets of red and blue points in the plane, and O be a set of k=2 lines passing through the origin. We study the problem of computing the set of orientations of the lines of O for which the O-convex hull of R contains no points of B. For k=2 orthogonal lines we have the rectilinear convex hull. In optimal O(nlogn) time and O(n) space, n=|R|+|B|, we compute the set of rotation angles such that, after simultaneously rotating the lines of O around the origin in the same direction, the rectilinear convex hull of R contains no points of B. We generalize this result to the case where O is formed by k=2 lines with arbitrary orientations. In the counter-clockwise circular order of the lines of O, let ai be the angle required to clockwise rotate the ith line so it coincides with its successor. We solve the problem in this case in O(1/T·NlogN) time and O(1/T·N) space, where T=min{a1,âŠ,ak} and N=max{k,|R|+|B|}. We finally consider the case in which O is formed by k=2 lines, one of the lines is fixed, and the second line rotates by an angle that goes from 0 to p. We show that this last case can also be solved in optimal O(nlogn) time and O(n) space, where n=|R|+|B|.Carlos AlegrĂa: Research supported by MIUR Proj. âAHeADâ no 20174LF3T8. David Orden:
Research supported by Project PID2019-104129GB-I00 / AEI / 10.13039/501100011033 of the Spanish
Ministry of Science and Innovation. Carlos Seara: Research supported by Project PID2019-104129GB-I00 /
AEI / 10.13039/501100011033 of the Spanish Ministry of Science and Innovation. Jorge Urrutia: Research
supported in part by SEP-CONACYThis project has received funding from the European Unionâs Horizon 2020 research and innovation
programme under the Marie SkĆodowskaâCurie Grant Agreement No 734922.Peer ReviewedPostprint (published version
Robust Bichromatic Classification using Two Lines
Given two sets and of at most points
in the plane, we present efficient algorithms to find a two-line linear
classifier that best separates the "red" points in from the "blue"
points in and is robust to outliers. More precisely, we find a region
bounded by two lines, so either a halfplane, strip,
wedge, or double wedge, containing (most of) the blue points , and
few red points. Our running times vary between optimal and
, depending on the type of region and whether
we wish to minimize only red outliers, only blue outliers, or both.Comment: 19 pages, 11 figures. Updated to include new result
Efficient Computation of Multiple Density-Based Clustering Hierarchies
HDBSCAN*, a state-of-the-art density-based hierarchical clustering method,
produces a hierarchical organization of clusters in a dataset w.r.t. a
parameter mpts. While the performance of HDBSCAN* is robust w.r.t. mpts in the
sense that a small change in mpts typically leads to only a small or no change
in the clustering structure, choosing a "good" mpts value can be challenging:
depending on the data distribution, a high or low value for mpts may be more
appropriate, and certain data clusters may reveal themselves at different
values of mpts. To explore results for a range of mpts values, however, one has
to run HDBSCAN* for each value in the range independently, which is
computationally inefficient. In this paper, we propose an efficient approach to
compute all HDBSCAN* hierarchies for a range of mpts values by replacing the
graph used by HDBSCAN* with a much smaller graph that is guaranteed to contain
the required information. An extensive experimental evaluation shows that with
our approach one can obtain over one hundred hierarchies for the computational
cost equivalent to running HDBSCAN* about 2 times.Comment: A short version of this paper appears at IEEE ICDM 2017. Corrected
typos. Revised abstrac
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