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Publications [#253070] of Stephen Mitroff

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  1. Salazar, E; Cain, MS; Darling, EF; Mitroff, SR; Carin, L (2012). Inferring latent structure from mixed real and categorical relational data. Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2, 1039-1046. [repository]
    (last updated on 2019/02/23)

    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).

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