FILIPPONE , Maurizio

Clustering methods provide an useful tool to tackle the problem of exploring large-dimensional data. However many common approaches suffer from being applied in high-dimensional spaces. Building on a dissimilarity-based representation of data, we propose a dimensionality reduction technique which preserves the clustering structure of the data....

The data representation strategy termed "Membership Embedding" is a type of similarity-based representation that uses a set of data items in an input space as reference points (probes), and represents all data in terms of their membership to the fuzzy concepts represented by the probes. The technique has been proposed as a concise...

We introduce a novel generative formulation of deep probabilistic models implementing "soft" constraints on their function dynamics. In particular, we develop a flexible methodological framework where the modeled functions and derivatives of a given order are subject to inequality or equality constraints. We then characterize the posterior...

Disease progression modeling (DPM) of Alzheimer's disease (AD) aims at revealing long term pathological trajectories from short term clinical data. Along with the ability of providing a data-driven description of the natural evolution of the pathology, DPM has the potential of representing a valuable clinical instrument for automatic diagnosis,...