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Inferring Latent Structure From Mixed Real and Categorical Relational Data

dc.contributor.author Salazar, E
dc.contributor.author Cain, MS
dc.contributor.author Darling, EF
dc.contributor.author Mitroff, SR
dc.contributor.author Carin, L
dc.date.accessioned 2014-07-22T16:18:52Z
dc.identifier http://arxiv.org/abs/1206.6469v1
dc.identifier.uri https://hdl.handle.net/10161/8953
dc.description.abstract We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix maps each row-column pair of binary feature vectors to an observed matrix element. The latent binary features of the rows are modeled via a multivariate Gaussian distribution with low-rank covariance matrix, and the Gaussian random variables are mapped to latent binary features via a probit link. The same type construction is applied jointly to the columns. The model infers latent, low-dimensional binary features associated with each row and each column, as well correlation structure between all rows and between all columns.
dc.publisher icml.cc / Omnipress
dc.subject cs.LG
dc.subject cs.LG
dc.subject stat.ML
dc.title Inferring Latent Structure From Mixed Real and Categorical Relational Data
dc.type Journal article
duke.contributor.id Salazar, E|0522904
duke.contributor.id Mitroff, SR|0374745
duke.contributor.id Carin, L|0100049
pubs.author-url http://arxiv.org/abs/1206.6469v1
pubs.notes Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)
pubs.organisational-group Center for Cognitive Neuroscience
pubs.organisational-group Duke
pubs.organisational-group Duke Institute for Brain Sciences
pubs.organisational-group Electrical and Computer Engineering
pubs.organisational-group Faculty
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Pratt School of Engineering
pubs.organisational-group Psychology and Neuroscience
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group University Institutes and Centers


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