Timezone: »
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 quantifying how the observations can be expressed as a combination of these patterns. We introduce the OrMachine, a probabilistic generative model for Boolean matrix factorisation and derive a Metropolised Gibbs sampler that facilitates efficient parallel posterior inference. On real world and simulated data, our method outperforms all currently existing approaches for Boolean matrix factorisation and completion. This is the first method to provide full posterior inference for Boolean Matrix factorisation which is relevant in applications, e.g. for controlling false positive rates in collaborative filtering and, crucially, improves the interpretability of the inferred patterns. The proposed algorithm scales to large datasets as we demonstrate by analysing single cell gene expression data in 1.3 million mouse brain cells across 11 thousand genes on commodity hardware.
Summary/Notes]
Author Information
Tammo Rukat (University of Oxford)
Christopher Holmes (University of Oxford)
Michalis Titsias (Athens University of Economics and Business)
Christopher Yau (University of Birmingham)
Related Events (a corresponding poster, oral, or spotlight)
-
2017 Talk: Bayesian Boolean Matrix Factorisation »
Mon Aug 7th 04:42 -- 05:00 AM Room C4.9& C4.10
More from the Same Authors
-
2021 Poster: Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections »
Alexander D Camuto · Xiaoyu Wang · Lingjiong Zhu · Christopher Holmes · Mert Gurbuzbalaban · Umut Simsekli -
2021 Spotlight: Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections »
Alexander D Camuto · Xiaoyu Wang · Lingjiong Zhu · Christopher Holmes · Mert Gurbuzbalaban · Umut Simsekli -
2019 Poster: Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap »
Edwin Fong · Simon Lyddon · Christopher Holmes -
2019 Oral: Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap »
Edwin Fong · Simon Lyddon · Christopher Holmes -
2018 Poster: Probabilistic Boolean Tensor Decomposition »
Tammo Rukat · Christopher Holmes · Christopher Yau -
2018 Oral: Probabilistic Boolean Tensor Decomposition »
Tammo Rukat · Christopher Holmes · Christopher Yau -
2018 Poster: Augment and Reduce: Stochastic Inference for Large Categorical Distributions »
Francisco Ruiz · Michalis Titsias · Adji Bousso Dieng · David Blei -
2018 Oral: Augment and Reduce: Stochastic Inference for Large Categorical Distributions »
Francisco Ruiz · Michalis Titsias · Adji Bousso Dieng · David Blei