The k-means++ algorithm is generalized by choosing the most distant point from the nearest center.
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For nearly spherical bodies, the unique center is proven under certain conditions.
We investigate centers of a body (the closure of a bounded open set) defined as maximum points of potentials. In particular, we study centers defined by the Riesz potential and by Poisson's integral. These centers, in general, depend on parameters and move with respect to the parameters. We give a necessary and suffici…
A new clustering method preserves data distribution.
Private learner for halfspaces with efficient sample complexity.
The object of our investigation is a point that gives the maximum value of a potential with a strictly decreasing radially symmetric kernel. It defines a center of a body in Rm. When we choose the Riesz kernel or the Poisson kernel as the kernel, such centers are called a radial center or an illuminating center, respec…
A variant of the Circle Packing Theorem states that the combinatorial class of any convex polyhedron contains elements midscribed to the unit sphere centered at the origin, and that these representatives are unique up to Möbius transformations of the sphere. Motivated by this result, various papers investigate the prob…
We prove the existence of a center, or continuous selection of a point, in the relative interior of embedded -disks in Riemannian -manifolds. If the center can be made equivariant with respect to the isometries of the manifold, and under mild assumptions the same holds for . By contrast, for…
New algorithm for clustering data streams with no substitutions.
Models for 3D harmonic 1-forms and spinors near singular points.
Determinantal consensus clustering improves clustering robustness.
Archimedes determined the center of gravity of a parabolic section as follows. For a parabolic section between a parabola and any chord on the parabola, let us denote by the point on the parabola where the tangent is parallel to and by the point where the line through parallel to the axis of the p…
We consider the two body problem with central interaction on two point homogeneous spaces from point of view of the invariant differential operators theory. The representation of the two particle Hamiltonian in terms of the radial differential operator and invariant operators on the symmetry group is found. The connect…
Paper explores online k-means clustering, finding optimal bounds for center count.
Simplified proof of mass center system's uniqueness and generalized Pappus' theorem across Euclidean, spherical, and hyperbolic geometries.
We determine the complete conjugate locus along all geodesics parallel or perpendicular to the center (Theorem 2.3). When the center is 1-dimensional we obtain formulas in all cases (Theorem 2.5), and when a certain operator is also diagonalizable these formulas become completely explicit (Corollary 2.7). These yield s…
In this paper we present a new method for motion tracking of tumors in liver ultrasound image sequences. Our algorithm has two main steps. In the first step, we apply mean shift algorithm with multiple features to estimate the center of the target in each frame. Target in the first frame is defined using an ellipse. Ed…
Study centers of generalized skein algebras, showing almost Azumaya properties.
New clustering method ensures fairness and community preservation.
We generalize the Riesz potential of a compact domain in by introducing a renormalization of the -potential for . This can be considered as generalization of the dual mixed volumes of convex bodies as introduced by Lutwak. We then study the points where the extreme values of the (renorm…
Analyzes branch points of area-minimizing currents with non-2 planar frequency.
The purpose of this article is to \begin{enumerate} \item define the -fold center of mass arrangement for points in the plane, \item give elementary properties of and \item give consequences concerning the space of distinct points in the plane, no four of which are the vertices of …
The aim of this paper is to train an RBF neural network and select centers under concurrent faults. It is well known that fault tolerance is a very attractive property for neural networks. And center selection is an important procedure during the training process of an RBF neural network. In this paper, we devise two n…
Algorithm provides fair clustering guarantees for k-means and k-median.
Proportional centroid clustering aims to fairly group points without prior protected subsets.
Develops a fair clustering algorithm for datasets with outliers.
The paper improves estimates of Gaussian curvature for minimal graphs over a unit disk.
The paper proves no point interactions exist for 3D sub-Laplacians.
Paper studies inscribed sphere and lines through centers of Apollonius spheres in n dimensions.
We define barycentric coordinates on a Riemannian manifold using Karcher's center of mass technique applied to point masses for n+1 sufficiently close points, determining an n-dimensional Riemannian simplex defined as a "Karcher simplex." Specifically, a set of weights is mapped to the Riemannian center of mass for the…
We construct a branched center manifold in a neighborhood of a singular point of a -dimensional integral current which is almost minimizing in a suitable sense. Our construction is the first half of an argument which shows the discreteness of the singular set for the following three classes of -dimensional curren…
We study the fixed point set in the ideal boundary of a parabolic isometry of a proper CAT(0)-space. We show that the radius of the fixed point set is at most pi/2, and study its centers. As a consequence, we prove that the set of fixed points is contractible with respect to the Tits topology.
The purpose of this article is to present a new regularization technique of quasi-plurisubharmoinc functions on a compact Kaehler manifold. The idea is to regularize the function on local coordinate balls first, and then glue each piece together. Therefore, all the higher order terms in the complex Hessian of this regu…
New algorithm finds k-centers from noisy distance estimates.
Study on Funk geometry volume growth and polytope flags, verifying conjectures.
We prove local Lipschitz property of the map which puts in correspondence to each --net different from --net its Chebyshev center. If dimension of Eucledean or Lobachevskii space is greater than 1 and net consists of more than 2 points we show that this map is not Lipschits in a neighbourhood of the space of …
This paper constructs a family of coordinate systems about a point on a quaternionic contact manifold, called quaternionic contact pseudohermitian normal coordinates. Once defined, conformal variations of the quaternionic contact structure induce changes on the coordinates which are studied in an effort to simplify the…
Gradient-based clustering method for various cost functions.
CoCP optimizes prediction intervals by jointly learning center and radius, improving efficiency and coverage.
Recently, Awasthi et al. introduced an SDP relaxation of the -means problem in . In this work, we consider a random model for the data points in which balls of unit radius are deterministically distributed throughout , and then in each ball, points are drawn according to a common ro…
Characterizes curves for minimal surfaces in de Sitter space.
We prove the existence of extremal domains for the first eigenvalue of the Laplace-Beltrami operator in some compact Riemannian manifolds of dimension , with volume close to the volume of the manifold. If the first (positive) eigenfunction of the Laplace-Beltrami operator over the manifold is a nonconst…
We find that for any n-dimensional, compact, convex subset K of R^{n+1} there is an affinely-spherical hypersurface M in R^{n+1} with center at the relative interior of K, such that the disjoint union of M and K is the boundary of an (n+1)-dimensional, compact, convex set. This so-called affine hemisphere M is uniquely…
Refined estimates for surfaces in curved spaces based on Willmore functional.
The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper provides detailed characterizations of deep learning models used in many Facebook social network services. We present computational characteristics of our models, describe high performance optimizati…
Finding the optimal -means clustering is NP-hard in general and many heuristics have been designed for minimizing monotonically the -means objective. We first show how to extend Lloyd's batched relocation heuristic and Hartigan's single-point relocation heuristic to take into account empty-cluster and single-poin…
Facial Key Points (FKPs) Detection is an important and challenging problem in the fields of computer vision and machine learning. It involves predicting the co-ordinates of the FKPs, e.g. nose tip, center of eyes, etc, for a given face. In this paper, we propose a LeNet adapted Deep CNN model - NaimishNet, to operate o…
We propose a new foliation of asymptotically Euclidean initial data sets by 2-spheres of constant spacetime mean curvature (STCMC). The leaves of the foliation have the STCMC-property regardless of the initial data set in which the foliation is constructed which asserts that there is a plethora of STCMC 2-spheres in a …