Eemeli Leppäaho

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We introduce Bayesian multi-tensor factorization, a model that is the first Bayesian formulation for joint factorization of multiple matrices and tensors. The research problem generalizes the joint matrix–tensor factorization problem to arbitrary sets of tensors of any depth, including matrices, can be interpreted as unsupervised multi-view learning from(More)
MOTIVATION Modelling methods that find structure in data are necessary with the current large volumes of genomic data, and there have been various efforts to find subsets of genes exhibiting consistent patterns over subsets of treatments. These biclustering techniques have focused on one data source, often gene expression data. We present a Bayesian(More)
The R package GFA provides a full pipeline for factor analysis of multiple data sources that are represented as matrices with co-occurring samples. It allows learning dependencies between subsets of the data sources, decomposed into latent factors. The package also implements sparse priors for the factorization, providing interpretable biclusters of the(More)
Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices, and giving principled predictions of missing values. However, scaling up MCMC samplers to large matrices has proven to be difficult with parallel algorithms that require communication between MCMC iterations. On the other hand, designing(More)
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