Complex systems are very often organized under the form of networks where nodes and
edges are embedded in space. Transportation and mobility networks, Internet, mobile phone …

Graphs are a powerful and popular representation formalism in pattern recognition.
Particularly in the field of document analysis they have found widespread application. From …

We present AI Poincaré, a machine learning algorithm for autodiscovering conserved
quantities using trajectory data from unknown dynamical systems. We test it on five …

Low-rank representation (LRR) has recently attracted a great deal of attention due to its
pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a …

Functional lateralisation is a fundamental principle of the human brain. However, a
comprehensive taxonomy of functional lateralisation and its organisation in the brain is …

In this paper, we examine the main advances registered in the last ten years in Pattern
Recognition methodologies based on graph matching and related techniques, analyzing …

In recent years the use of graph based representation has gained popularity in pattern
recognition and machine learning. As a matter of fact, object representation by means of …

Graph clustering is an area in cluster analysis that looks for groups of related vertices in a
graph. Due to its large applicability, several graph clustering algorithms have been …

The observation and description of the living brain has attracted a lot of research over the
past centuries. Many noninvasive imaging modalities have been developed, such as …

Because the properties of objects are largely determined by their geometric features, shape
analysis and classification are essential to almost every applied scientific and technological …