Functional Mixed Membership Models
Nicholas De Marco, Damla Şentürk, Shafali Jeste, Charlotte DiStefano, Abigail Dickinson, Donatello Telesca
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Mixed membership models, or partial membership models, are a flexible unsupervised learning method that allows each observation to belong to multiple clusters. In this paper, we propose a Bayesian mixed membership model for functional data. By using the multivariate Karhunen-Loève theorem, we are able to derive a scalable representation of Gaussian processes that maintains data-driven learning of the covariance struc ...
ture. Within this framework, we establish conditional posterior consistency given a known feature allocation matrix. Compared to previous work on mixed membership models, our proposal allows for increased modeling flexibility, with the benefit of a directly interpretable mean and covariance structure. Our work is motivated by studies in functional brain imaging through electroencephalography (EEG) of children with autism spectrum disorder (ASD). In this context, our work formalizes the clinical notion of "spectrum" in terms of feature membership proportions. Supplementary materials, including proofs, are available online. The R package BayesFMMM is available to fit functional mixed membership models.
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Mixed membership models, or partial membership models, are a flexible unsupervised learning method that allows each observation to belong to multiple clusters.
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4
Citations
38
References
Tasks
Covariance, Context (archaeology), Representation (politics), Computer science, Feature (linguistics), Consistency (knowledge bases), Pattern recognition (psychology), Data mining
Methods
Bayesian probability
Domains
Artificial intelligence, Machine learning, Mathematics
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