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Joint Modeling of a Matrix with Associated Text via Latent Binary Features
XianXing Zhang · Lawrence Carin

Thu Dec 06 02:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

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 latent binary features (topics) 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. State-of-the-art results are manifested for prediction of votes on a new piece of legislation, based only on the observed text legislation. The coupling of the text and legislation is also demonstrated to yield insight into the properties of the matrix decomposition for roll-call data.

Author Information

XianXing Zhang (Duke University)
Lawrence Carin (KAUST)

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