Support vector machines (SVM) are attractive for the classification of remotely sensed data
with some claims that the method is insensitive to the dimensionality of the data and …

Modern data analysis tools have to work on high-dimensional data, whose components are
not independently distributed. High-dimensional spaces show surprising, counter-intuitive …

Stein variational gradient descent (SVGD) was recently proposed as a general purpose
nonparametric variational inference algorithm: it minimizes the Kullback–Leibler divergence …

In many applications, input data are sampled functions taking their values in infinite-
dimensional spaces rather than standard vectors. This fact has complex consequences on …

Recently, anomaly detection (AD) has attracted considerable interest in a wide variety of
hyperspectral remote sensing applications. The goal of this unsupervised technique of target …

Mapping high-dimensional data in a low-dimensional space, for example, for visualization,
is a problem of increasingly major concern in data analysis. This paper presents data-driven …

We propose in this work RBM-SVGD, a stochastic version of the Stein variational gradient
descent (SVGD) method for efficiently sampling from a given probability measure, which is …

We introduce a new variational inference (VI) framework, called energetic variational
inference (EVI). It minimizes the VI objective function based on a prescribed energy …

Dimension reduction (DR) is important in the processing of data in domains such as
multimedia or bioinformatics because such data can be of very high dimension. Dimension …

Abstract Kernel methods (Cristianini and Shawe-Taylor 2000; Herbrich 2002; Schölkopf and
Smola 2002; Shawe-Taylor and Cristianini 2004) can be regarded as machine learning …