Inferring latent structure from mixed real and categorical relational data

Esther Salazar, Matthew S. Cain, Elise F. Darling, Stephen R. Mitroff, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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. Copyright 2012 by the author(s)/owner(s).
Original languageEnglish (US)
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Pages1039-1046
Number of pages8
StatePublished - Oct 10 2012
Externally publishedYes

Fingerprint Dive into the research topics of 'Inferring latent structure from mixed real and categorical relational data'. Together they form a unique fingerprint.

Cite this