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Distance Dependent Infinite Latent Feature Models

Author(s): Gershman, Samuel J; Frazier, Peter I; Blei, David M

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Abstract: Latent feature models are widely used to decompose data into a small number of components. Bayesian nonparametric variants of these models, which use the Indian buffet process (IBP) as a prior over latent features, allow the number of features to be determined from the data. We present a generalization of the IBP, the distance dependent Indian buffet process (dd-IBP), for modeling non-exchangeable data. It relies on distances defined between data points, biasing nearby data to share more features. The choice of distance measure allows for many kinds of dependencies, including temporal and spatial. Further, the original IBP is a special case of the dd-IBP. We develop the dd-IBP and theoretically characterize its feature-sharing properties. We derive a Markov chain Monte Carlo sampler for a linear Gaussian model with a dd-IBP prior and study its performance on real-world non-exchangeable data.
Publication Date: 2014
Citation: Gershman, Samuel J., Peter I. Frazier, and David M. Blei. "Distance Dependent Infinite Latent Feature Models." IEEE Transactions on Pattern Analysis and Machine Intelligence 37, no. 2 (2014): pp. 334-345. doi:10.1109/TPAMI.2014.2321387
DOI: 10.1109/TPAMI.2014.2321387
ISSN: 0162-8828
EISSN: 1939-3539
Pages: 334 - 345
Type of Material: Journal Article
Journal/Proceeding Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
Version: Author's manuscript

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