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Estimating Maximally Probable Constrained Relations by Mathematical Programming

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45231

Qu,  Lizhen
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons98382

Andres,  Bjoern
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Fulltext (public)

1408.0838.pdf
(Preprint), 522KB

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Citation

Qu, L., & Andres, B. (2014). Estimating Maximally Probable Constrained Relations by Mathematical Programming. Retrieved from http://arxiv.org/abs/1408.0838.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0024-D324-6
Abstract
Estimating a constrained relation is a fundamental problem in machine learning. Special cases are classification (the problem of estimating a map from a set of to-be-classified elements to a set of labels), clustering (the problem of estimating an equivalence relation on a set) and ranking (the problem of estimating a linear order on a set). We contribute a family of probability measures on the set of all relations between two finite, non-empty sets, which offers a joint abstraction of multi-label classification, correlation clustering and ranking by linear ordering. Estimating (learning) a maximally probable measure, given (a training set of) related and unrelated pairs, is a convex optimization problem. Estimating (inferring) a maximally probable relation, given a measure, is a 01-linear program. It is solved in linear time for maps. It is NP-hard for equivalence relations and linear orders. Practical solutions for all three cases are shown in experiments with real data. Finally, estimating a maximally probable measure and relation jointly is posed as a mixed-integer nonlinear program. This formulation suggests a mathematical programming approach to semi-supervised learning.