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Spectral geometric methods in practice

Total Cost €


EC-Contrib. €






 SPECGEO project word cloud

Explore the words cloud of the SPECGEO project. It provides you a very rough idea of what is the project "SPECGEO" about.

decomposition    acceptance    efforts    primarily    corruption    data    pervasive    uses    bounds    analytical    network    transformations    largely    suboptimal    fourier    employed    infeasible    dimensional    branches    ranging    small    techniques    theory    picture    epsilon    fact    domains    machine    operator    world    limited    vision    directed    operators    cross    settings    toward    perturbation    linear    learning    contradicts    devoted    behavior    geometric    eigendecomposition    lies    modality    lack    framework    graphs    models    biology    little    kinds    science    inconsistency    removal    missing    abstract    single    mainly    constructed    geometry    incompleteness    apparent    spectral    undergoing    arbitrary    despite    instability    corrupted    tools    computer    analogous    induce    presumption    motivated    outstanding    fundamentally    adoption    theoretical    contending    valuable    point    crude    computational    surfaces    interpreted    view    deal   

Project "SPECGEO" data sheet

The following table provides information about the project.


Organization address
address: Piazzale Aldo Moro 5
city: ROMA
postcode: 185

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Italy [IT]
 Total cost 1˙434˙000 €
 EC max contribution 1˙434˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-09-01   to  2023-08-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Spectral geometry concerns the study of the geometric properties of data domains, such as surfaces or graphs, via the spectral decomposition of linear operators defined upon them. Due to their valuable properties analogous to Fourier theory, such methods find widespread use in several branches of computer science, ranging from computer vision to machine learning and network analysis.

Despite their pervasive presence, very little efforts have been devoted to the design and application of spectral techniques that deal with corrupted, missing, high-dimensional or abstract data undergoing complex transformations. This lack of focus is mainly motivated by the widespread acceptance, supported in part by theoretical results, that an ε-perturbation to the geometry of the data (as small as the removal of a single point) can induce arbitrary changes in the operator’s eigendecomposition – leading to a limited adoption of spectral models in real-world applications. This project challenges this view, contending that such presumption of instability is primarily due to a suboptimal choice of the analytical tools that are currently being employed, and which only provide part of the picture. In fact, strong evidence largely contradicts the expected behavior on real geometric data. The reason behind this apparent inconsistency lies in the different focus of current methods, which provide crude bounds and are directed toward other kinds of perturbation than those observed in real settings.

The ambitious goal of this project is to develop a novel theoretical and computational framework that will fundamentally change the way spectral techniques are constructed, interpreted, and applied. These tools will enable a range of currently infeasible uses of spectral methods on real data. They will deal with strong incompleteness, corruption and cross-modality, and they will be applied to outstanding problems in geometry processing, computer vision, machine learning, and computational biology.

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The information about "SPECGEO" are provided by the European Opendata Portal: CORDIS opendata.

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