---
title: "Scalable Probabilistic Tensor Factorization for Binary and Count Data"
authors:
  - "Piyush Rai"
  - "Changwei Hu"
  - "Matthew Harding"
  - "Lawrence Carin"
doi: ""
url: "https://ijcai.org/proceedings/2015/530"
pdf: "https://ijcai.org/proceedings/2015/0530.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "3770-3775"
track: "Special Track on Machine Learning"
keywords:
---

# Scalable Probabilistic Tensor Factorization for Binary and Count Data

Piyush Rai, Changwei Hu, Matthew Harding, Lawrence Carin

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 3770-3775.

## Abstract

Tensor factorization methods provide a useful way to extract latent factors from complex multirelational data, and also for predicting missing data. Developing tensor factorization methods for massive tensors, especially when the data are binary- or count-valued (which is true of most real-world tensors), however, remains a challenge. We develop a scalable probabilistic tensor factorization framework that enables us to perform efficient factorization of massive binary and count tensor data. The framework is based on (i) the Polya-Gamma augmentation strategy which makes the model fully locally conjugate and allows closed-form parameter updates when data are binary- or count-valued; and (ii) an efficient online Expectation Maximization algorithm, which allows processing data in small mini-batches, and facilitates handling massive tensor data. Moreover, various types of constraints on the factor matrices (e.g., sparsity, non-negativity) can be incorporated under the proposed framework, providing good interpretability, which can be useful for qualitative analyses of the results. We apply the proposed framework on analyzing several binary- and count-valued real-world data sets.