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Noisy and Incomplete Boolean Matrix Factorizationvia Expectation Maximization
Probabilistic approach to Boolean matrix factorization can provide solut...
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The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization
Boolean matrix factorization (BMF) is a popular and powerful technique f...
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Biclustering and Boolean Matrix Factorization in Data Streams
We study the clustering of bipartite graphs and Boolean matrix factoriza...
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From-Below Boolean Matrix Factorization Algorithm Based on MDL
During the past few years Boolean matrix factorization (BMF) has become ...
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The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization
Mining and exploring databases should provide users with knowledge and n...
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MEBF: a fast and efficient Boolean matrix factorization method
Boolean matrix has been used to represent digital information in many fi...
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Distributed Bayesian Matrix Factorization with Minimal Communication
Bayesian matrix factorization (BMF) is a powerful tool for producing low...
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C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization
Given labeled data represented by a binary matrix, we consider the task to derive a Boolean matrix factorization which identifies commonalities and specifications among the classes. While existing works focus on rank-one factorizations which are either specific or common to the classes, we derive class-specific alterations from common factorizations as well. Therewith, we broaden the applicability of our new method to datasets whose class-dependencies have a more complex structure. On the basis of synthetic and real-world datasets, we show on the one hand that our method is able to filter structure which corresponds to our model assumption, and on the other hand that our model assumption is justified in real-world application. Our method is parameter-free.
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