Improved Local Search for Binary Matrix Factorization

Hamid Mirisaee 1 Eric Gaussier 1 Alexandre Termier 2
2 DREAM - Diagnosing, Recommending Actions and Modelling
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : Rank K Binary Matrix Factorization (BMF) approximates a binary matrix by the product of two binary matrices of lower rank, K, using either L1 or L2 norm. In this paper, we first show that the BMF with L2 norm can be reformulated as an Unconstrained Binary Quadratic Programming (UBQP) problem. We then review several local search strategies that can be used to improve the BMF solutions obtained by previously proposed methods, before introducing a new local search dedicated to the BMF problem. We show in particular that the proposed solution is in general faster than the previously proposed ones. We then assess its behavior on several collections and methods and show that it significantly improves methods targeting the L2 norms on all the datasets considered; for the L1 norm, the improvement is also significant for real, structured datasets and for the BMF problem without the binary reconstruction constraint.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01178773
Contributor : Alexandre Termier <>
Submitted on : Monday, July 20, 2015 - 7:56:58 PM
Last modification on : Saturday, December 15, 2018 - 1:49:25 AM

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  • HAL Id : hal-01178773, version 1

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Hamid Mirisaee, Eric Gaussier, Alexandre Termier. Improved Local Search for Binary Matrix Factorization. Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI, Jan 2015, Austin, United States. pp.1198-1204. ⟨hal-01178773⟩

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