Technological and theoretical advances in all scientific disciplines ranging from Engineering to Sciences have provided us with numerous large scale datasets to analyze. A fundamental question is how extract low dimensional models that will reflect in an efficient manner the most-important states and dynamics of the system under study. To tackle with such problems this symposium confront with two challenges: that of dimensionality reduction and the problem of data mining. Algorithms and methods that have the potential to facilitate better understanding, predicting and modelling of large-scale data and images with important health, social and economical impact will be discussed.
- On the generation of reduced models by Proper Orthogonal Decomposition from experimental image data
- Lucia Russo (Consiglio Nazionale delle Ricerche, Istituto di Ricerche sulla Combustione)
- Intrinsic Isometric Manifold Learning with Application to Unsupervised Localization from Image Data
- Ariel Schwartz (Technion - Israel Institute of Technology)
- Construction of low dimensional Functional connectivity networks from fMRI data using manifold learning algorithms
- Costantinos Siettos (National Technical University of Athens )
- Contrast enhancement operators based on attractors identification in nonlinear dynamical systems
- Jacques Demongeot (University of Grenoble Alpes (UGA))
- Neural Manifolds: Sparse Dictionary Learning Approaches
- Francesco Donnarumma (Institute of Cognitive Sciences and Technologies, Rome)
- Application of the Optical Flow Method for the analysis of flame propagation in a transparent internal combustion engine
- Simone Lombardi (Università degli Studi del Sannio)
- Application of Decomposition Methods to the study of flames via image sequences
- Gaetano Continillo (University of Sannio, Benevento)
- Organizers:
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Salvatore Cuomo (Dept. Mathematics and Applications "Renato Caccioppoli", University of Naples)
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Lucia Russo (Consiglio Nazionale delle Ricerche, Istituto di Ricerche sulla Combustione)
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Costantinos Siettos (National Technical University of Athens )
- Keywords:
- artificial intelligence techniques, bayesian methods, computer graphics, image enhancement, image from combustion problems, image representation, inverse problems, machine learning, pattern recognition