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Low-rank Boolean matrix approximation by integer programming

Abstract:
Low-rank approximations of data matrices are an important dimensionality re- duction tool in machine learning and regression analysis. We consider the case of categorical variables, where it can be formulated as the problem of finding low-rank approximations to Boolean matrices. In this paper we give what is to the best of our knowledge the first integer programming formulation that relies on only polynomially many variables and constraints, we discuss how to solve it computationally and report numerical tests on synthetic and real-world data.
Publication status:
Not published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
Pembroke College
Role:
Author
ORCID:
0000-0002-1166-5329
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Name:
St Cross College, Oxford
Grant:
Robin & Nadine Wells Scholarship
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/N510129/1
Host title:
10th NIPS Workshop on Optimization for Machine Learning (OPT 2017)
Journal:
10th NIPS Workshop on Optimization for Machine Learning (OPT 2017) More from this journal
Publication date:
2018-12-07
Acceptance date:
2017-10-24
Pubs id:
pubs:827223
UUID:
uuid:1e2e2c9a-d8de-46a2-b987-331cb8641b2e
Local pid:
pubs:827223
Source identifiers:
827223
Deposit date:
2018-03-01

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