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Non-negative matrix factorization for discrete data with hierarchical side-information

Publication ,  Conference
Hu, C; Rai, P; Carin, L
Published in: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016
January 1, 2016

We present a probabilistic framework for efficient non-negative matrix factorization of discrete (count/binary) data with side-information. The side-information is given as a multi-level structure, taxonomy, or ontology, with nodes at each level being categorical-valued observations. For example, when modeling documents with a two-level side-information (documents being at level-zero), level-one may represent (one or more) authors associated with each document and level-two may represent affiliations of each author. The model easily generalizes to more than two levels (or taxonomy/ontology of arbitrary depth). Our model can learn embeddings of entities present at each level in the data/side-information hierarchy (e.g., documents, authors, affiliations, in the previous example), with appropriate sharing of information across levels. The model also enjoys full local conjugacy, facilitating efficient Gibbs sampling for model inference. Inference cost scales in the number of non-zero entries in the data matrix, which is especially appealing for real-world massive but sparse matrices. We demonstrate the effectiveness of the model on several real-world data sets.

Duke Scholars

Published In

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016

Publication Date

January 1, 2016

Start / End Page

1124 / 1132
 

Citation

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Hu, C., Rai, P., & Carin, L. (2016). Non-negative matrix factorization for discrete data with hierarchical side-information. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016 (pp. 1124–1132).
Hu, C., P. Rai, and L. Carin. “Non-negative matrix factorization for discrete data with hierarchical side-information.” In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, 1124–32, 2016.
Hu C, Rai P, Carin L. Non-negative matrix factorization for discrete data with hierarchical side-information. In: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016. 2016. p. 1124–32.
Hu, C., et al. “Non-negative matrix factorization for discrete data with hierarchical side-information.” Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, 2016, pp. 1124–32.
Hu C, Rai P, Carin L. Non-negative matrix factorization for discrete data with hierarchical side-information. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016. 2016. p. 1124–1132.

Published In

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016

Publication Date

January 1, 2016

Start / End Page

1124 / 1132