Given a dataset D partitioned in clusters, the joint distance function (JDF)J(x) at any point x is the harmonic mean of the distances between x and the cluster centers. The JDF is a continuous function, capturing the data points in its lower level sets (a property called contour approximation), and is a useful concept in probabilistic clustering and data analysis
The cluster assumption had a significant impact on the reasoning behind semi-supervised classi-ficat...
We apply the techniques of computable model theory to the distance function of a graph. This task le...
Abstract. Distance function to a compact set plays a central role in several areas of computational ...
Abstract. Given a dataset D partitioned in clusters, the joint distance function (JDF) J(x) at any p...
Abstract. A contour approximation of data is a function capturing the data points in its lower level...
The probabilistic distance clustering method of the authors [2, 8], assumes the cluster membership p...
K-means is a widely used partitional clustering method. A large amount of effort has been made on fi...
In this paper we define distance functions for data sets (and distributions) in a RKHS context. To ...
Inferring and comparing complex, multivariable probability density functions is fundamental to probl...
We propose a new class of metrics on sets, vectors, and functions that can be used in various stages...
The learning metrics principle describes a way to derive metrics to the data space from paired data...
Distance correlation is a measure of the relationship between random vectors in arbitrary dimension....
A framework is developed for inference concerning the covariance operator of a functional random pr...
Clustering, Probabilistic clustering, Mahalanobis distance, Harmonic mean, Joint distance function, ...
The topic of this paper is the distribution of the distance between two points distributed independe...
The cluster assumption had a significant impact on the reasoning behind semi-supervised classi-ficat...
We apply the techniques of computable model theory to the distance function of a graph. This task le...
Abstract. Distance function to a compact set plays a central role in several areas of computational ...
Abstract. Given a dataset D partitioned in clusters, the joint distance function (JDF) J(x) at any p...
Abstract. A contour approximation of data is a function capturing the data points in its lower level...
The probabilistic distance clustering method of the authors [2, 8], assumes the cluster membership p...
K-means is a widely used partitional clustering method. A large amount of effort has been made on fi...
In this paper we define distance functions for data sets (and distributions) in a RKHS context. To ...
Inferring and comparing complex, multivariable probability density functions is fundamental to probl...
We propose a new class of metrics on sets, vectors, and functions that can be used in various stages...
The learning metrics principle describes a way to derive metrics to the data space from paired data...
Distance correlation is a measure of the relationship between random vectors in arbitrary dimension....
A framework is developed for inference concerning the covariance operator of a functional random pr...
Clustering, Probabilistic clustering, Mahalanobis distance, Harmonic mean, Joint distance function, ...
The topic of this paper is the distribution of the distance between two points distributed independe...
The cluster assumption had a significant impact on the reasoning behind semi-supervised classi-ficat...
We apply the techniques of computable model theory to the distance function of a graph. This task le...
Abstract. Distance function to a compact set plays a central role in several areas of computational ...
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