Advances in data collection and storage have tremendously increased the presence of
functional data, whose graphical representations are curves, images or shapes. As a new …

The application of graph analytics to various domains has yielded tremendous societal and
economical benefits in recent years. However, the increasingly widespread adoption of …

Private companies, government entities, and institutions such as hospitals routinely gather
vast amounts of digitized personal information about the individuals who are their …

A hierarchical Bayesian model is considered for decomposing a matrix into low-rank and
sparse components, assuming the observed matrix is a superposition of the two. The matrix …

The principal components analysis (PCA) algorithm is a standard tool for identifying good
lowdimensional approximations to high-dimensional data. Many data sets of interest contain …

ABSTRACT Markov chain Monte Carlo methods explicitly defined on the manifold of
probability distributions have recently been established. These methods are constructed …

Principal components analysis (PCA) is a standard tool for identifying good low-dimensional
approximations to data sets in high dimension. Many current data sets of interest contain …

Finite mixtures of von Mises-Fisher distributions allow to apply model-based clustering
methods to data which is of standardized length, ie, all data points lie on the unit sphere. The …

In this paper, we focus on differential privacy preserving spectral graph analysis. Spectral
graph analysis deals with the analysis of the spectra (eigenvalues and eigenvector …

Traditional MCMC methods are only applicable to distributions which can be defined
on\mathbbR^ n. However, there exist many application domains where the distributions …