Generalizing the Convex Hull of a Sample: The R Package alphahull

This paper presents the R package alphahull which implements the ñ-convex hull and the ñ-shape of a finite set of points in the plane. These geometric structures provide an informative overview of the shape and properties of the point set. Unlike the convex hull, the ñ-convex hull and the ñ-shape are able to reconstruct non-convex sets. This flexibility make them specially useful in set estimation. Since the implementation is based on the intimate relation of theses constructs with Delaunay triangulations, the R package alphahull also includes functions to compute Voronoi and Delaunay tesselations. The usefulness of the package is illustrated with two small simulation studies on boundary length estimation.

Polynomial volume estimation and its applications

Given a compact set S ⊂ R d we consider the problem of estimating, from a random sample of points, the Lebesgue measure of S, µ(S), and its boundary measure, L(S) (as defined by the Minkowski content of ∂S). This topic has received some attention, especially in the two-dimensional case d = 2, motivated by applications in image analysis. A new method to simultaneously estimate µ(S) and L(S) from a sample of points inside S is proposed. The basic idea is to assume that S has a polynomial volume, that is, that V (r) := µ{x : d(x, S) ≤ r} is a polynomial in r of degree d, for all r in some interval [0, R). We develop a minimum distance approach to estimate the coefficients of V (r) and, in particular µ(S) and L(S), which correspond, respectively, to the independent term and the first degree coefficient of V (r). The strong consistency of the proposed estimators is proved. Some numerical illustrations are givenThis work has been partially supported by Spanish Grants MTM2016-78751-P (A. Cuevas) and MTM2016-76969-P (B. Pateiro-López)S

Testing uniformity for the case of a planar unknown support

The definitive version is available at http://www3.interscience.wiley.co

A multivariate uniformity test for the case of unknown support

The final publication is available at http://dx.doi.org/10.1007/s11222-010-9222-

On statistical properties of sets fulfilling rolling-type conditions

Motivated by set estimation problems, we consider three closely related shape conditions for compact sets: positive reach, r-convexity and rolling condition. First, the relations between these shape conditions are analyzed. Second, we obtain for the estimation of sets fulfilling a rolling condition a result of "full consistency" (i.e., consistency with respect to the Hausdorff metric for the target set and for its boundary). Third, the class of uniformly bounded compact sets whose reach is not smaller than a given constant r is shown to be a P-uniformity class (in Billingsley and Topsoe's (1967) sense) and, in particular, a Glivenko-Cantelli class. Fourth, under broad conditions, the r-convex hull of the sample is proved to be a fully consistent estimator of an r-convex support in the two-dimensional case. Moreover, its boundary length is shown to converge (a.s.) to that of the underlying support. Fifth, the above results are applied to get new consistency statements for level set estimators based on the excess mass methodology (Polonik, 1995)

A New Approach for Sparse Matrix Classification Based on Deep Learning Techniques

In this paper, a new methodology to select the best storage format for sparse matrices based on deep learning techniques is introduced. We focus on the selection of the proper format for the sparse matrix-vector multiplication (SpMV), which is one of the most important computational kernels in many scientific and engineering applications. Our approach considers the sparsity pattern of the matrices as an image, using the RGB channels to code several of the matrix properties. As a consequence, we generate image datasets that include enough information to successfully train a Convolutional Neural Network (CNN). Considering GPUs as target platforms, the trained CNN selects the best storage format 90.1% of the time, obtaining 99.4% of the highest SpMV performance among the tested formatsThis work has been supported by MINECO (TIN2014-54565-JIN and MTM2016-76969-P), Xunta de Galicia (ED431G/08) and European Regional Development Fun

On standardness and the non-estimability of certain functionals of a set

Standardness is a popular assumption in the literature on set estimation. It also appears in statistical approaches to topological data analysis, where it is common to assume that the data were sampled from a probability measure that satisfies the standard assumption. Relevant results in this field, such as rates of convergence and confidence sets, depend on the standardness parameter, which in practice may be unknown. In this paper, we review the notion of standardness and its connection to other geometrical restrictions. We prove the almost sure consistency of a plug-in type estimator for the so-called standardness constant, already studied in the literature. We propose a method to correct the bias of the plug-in estimator and corroborate our theoretical findings through a small simulation study. We also show that it is not possible to determine, based on a finite sample, whether a probability measure satisfies the standard assumption

Generalizing the convex hull of a sample: The R package alphahull

This vignette presents the R package alphahull which implements the α-convex hull and the α-shape of a finite set of points in the plane. These geometric structures provide an informative overview of the shape and properties of the point set. Unlike the convex hull, the α-convex hull and the α-shape are able to reconstruct non-convex sets. This flexibility make them specially useful in set estimation. Since the implementation is based on the intimate relation of theses constructs with Delaunay triangulations, the R package alphahull also includes functions to compute Voronoi and Delaunay tesselations