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Robust and reliable low-rank approximation and dimension reduction with COlumn subset SElection

Objective

Modern science and technology generate massive datasets, from genomic profiles to social networks and sensor recordings. To extract knowledge, we need to reduce their dimension without losing interpretability: the Column Subset Selection Problem (CSSP) tackles this problem by choosing a small set of representative columns from a matrix that retain the essential structure of the data. All other columns can be written, approximately, as a linear combination of the few chosen representative columns.

Real data are noisy, incomplete, and subject to external restrictions, be it fairness requirements across sub-populations, physical limits on sensor placement, or computational barriers to accessing the full dataset. While powerful, most current CSSP assume ideal data and unconstrained choices.

The COSE project (Robust and reliable low-rank approximation and dimension reduction with COlumn subset SElection) will tackle this fundamental challenge by developing a unified theoretical and computational framework for fast, robust, and constraint-aware CSSP. The main objectives of COSE are to address (1) robustness to perturbations, noise, and outliers; (2) integration of practical constraints, such as group fairness or limited data access; (3) theoretical guarantees that bridge the gap between heuristic success and rigorous understanding.

COSE will combine insights from numerical linear algebra, randomized algorithms, and optimization to develop provably reliable methods that scale to massive datasets. The outcomes will include new algorithms with theoretical guarantees, open-source software and benchmarks for robust low-rank approximation and dimension reduction, with advances that impact a broad range of fields, from scientific computing to machine learning.

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2026-STG

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Host institution

UNIVERSITA DI PISA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 362 108,00
Address
LUNGARNO PACINOTTI 43/44
56126 Pisa
Italy

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Region
Centro (IT) Toscana Pisa
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 362 108,00

Beneficiaries (1)

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