Joint modeling of a matrix with associated text via latent binary features

Xian Xing Zhang, Lawrence Carin

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

7 Scopus citations

Abstract

A new methodology is developed for joint analysis of a matrix and accompanying documents, with the documents associated with the matrix rows/columns. The documents are modeled with a focused topic model, inferring interpretable latent binary features for each document. A new matrix decomposition is developed, with latent binary features associated with the rows/columns, and with imposition of a low-rank constraint. The matrix decomposition and topic model are coupled by sharing the latent binary feature vectors associated with each. The model is applied to roll-call data, with the associated documents defined by the legislation. Advantages of the proposed model are demonstrated for prediction of votes on a new piece of legislation, based only on the observed text of legislation. The coupling of the text and legislation is also shown to yield insight into the properties of the matrix decomposition for roll-call data.
Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems
Pages1556-1564
Number of pages9
StatePublished - Dec 1 2012
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09

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