Opendata, web and dolomites


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.

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

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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