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A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data

Monti, RP; Hyvärinen, A; (2018) A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data. In: Proceedings of Conference on Uncertainty in Artificial Intelligence - UAI 2018. Uncertainty in Artificial Intelligence (UAI): USA. Green open access

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Abstract

Connectivity estimation is challenging in the context of high-dimensional data. A useful preprocessing step is to group variables into clusters, however, it is not always clear how to do so from the perspective of connectivity estimation. Another practical challenge is that we may have data from multiple related classes (e.g., multiple subjects or conditions) and wish to incorporate constraints on the similarities across classes. We propose a probabilistic model which simultaneously performs both a grouping of variables (i.e., detecting community structure) and estimation of connectivities between the groups which correspond to latent variables. The model is essentially a factor analysis model where the factors are allowed to have arbitrary correlations, while the factor loading matrix is constrained to express a community structure. The model can be applied on multiple classes so that the connectivities can be different between the classes, while the community structure is the same for all classes. We propose an efficient estimation algorithm based on score matching, and prove the identifiability of the model. Finally, we present an extension to directed (causal) connectivities over latent variables. Simulations and experiments on fMRI data validate the practical utility of the method.

Type: Proceedings paper
Title: A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data
Event: Conference on Uncertainty in Artificial Intelligence - UAI 2018
Open access status: An open access version is available from UCL Discovery
Publisher version: http://auai.org/uai2018/proceedings/papers/123.pdf
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/10049485
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