In MRI, the main magnetic field polarizes the electron cloud of a molecule, generating a
chemical shift for observer protons within the molecule and a magnetic susceptibility …

Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

This book addresses some theoretical aspects of semisupervised learning (SSL). The book
is organized as a collection of different contributions of authors who are experts on this topic …

Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

Distance metric is a key issue in many machine learning algorithms. This paper considers a
general problem of learning from pairwise constraints in the form of must-links and cannot …

Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …

In the IoT and WSN era, large number of connected objects and sensing devices are
dedicated to collect, transfer, and generate a huge amount of data for a wide variety of fields …

We address the problem of learning distance metrics using side-information in the form of
groups of" similar" points. We propose to use the RCA algorithm, which is a simple and …

Finite mixture models have a long history in statistics, having been used to model population
heterogeneity, generalize distributional assumptions, and lately, for providing a convenient …

Unsupervised co-analysis of a set of shapes is a difficult problem since the geometry of the
shapes alone cannot always fully describe the semantics of the shape parts. In this paper …