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Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the other quanti- fying how the observations can be expressed as a combination of these patterns.
Feb 20, 2017 · The proposed algorithm scales to large datasets as we demonstrate by analysing single cell gene expression data in 1.3 million mouse brain cells ...
Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing ...
This is the first method to provide full posterior inference for Boolean Matrix factorisation which is relevant in applications, e.g. for controlling false ...
Feb 25, 2017 · Boolean matrix factorisation aims to decompose a binary data matrix into an approximate. Boolean product of two low rank, binary matrices: ...
Boolean matrix factorization (BMF) aims to find an approxi- mation of a binary matrix as the Boolean product of two low rank Boolean matrices, which could ...
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Aug 6, 2017 · Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: ...
A unified approach to Boolean matrix factorization is introduced in this paper. •. The approach allows combining rank-1 binary terms with arbitrary Boolean ...
The goal of Boolean Matrix Factorization (BMF) is to approximate a given binary matrix as the product of two low-rank binary factor matrices, where the product ...
Our method outperforms all currently existing approaches for Boolean Matrix factorization and completion, as we show on simulated and real world data. This is ...