Warped Matrix Factorisation for Multi-view Data Integration

  • Naruemon PratanwanichEmail author
  • Pietro Lió
  • Oliver StegleEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9852)


Matrix factorisation is a widely used tool with applications in collaborative filtering, image analysis and in genomics. Several extensions of the classical model have been proposed, such as modelling of multiple related “data views” or accounting for side information on the latent factors. However, as the complexity of these models increases even subtle mismatches of the distributional assumptions on the input data can severely affect model performance. Here, we propose a simple yet effective solution to address this problem by modelling the observed data in a transformed or warped space. We derive a joint model of a multi-view matrix factorisation model that infers view-specific data transformations and provide a computationally efficient variational approximation for parameter inference. We first validate the model on synthetic data before applying it to a matrix completion problem in genomics. We show that our model improves the imputation of missing values in gene-disease association analysis and allows for discovering enhanced consensus structures across multiple data views The data and software related to this paper are available at


Multi-view learning Matrix factorisation Data transformation Side information 



This work was supported by Open Targets. We are thankful to Dr. Ian Dunham, Dr. Gautier Koscielny, Dr. Samiul Hasan, and Dr. Andrea Pierleoni for their helpful discussion and contributions in curating and managing the data we have used for the described experiments. NP has received support from the Royal Thai Government Scholarship.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.European Molecular Biology LaboratoryEuropean Bioinformatics InstituteCambridgeUK
  2. 2.Open TargetsCambridgeUK
  3. 3.Computer LaboratoryUniversity of CambridgeCambridgeUK

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