{"type":"ScholarlyArticle","title":"Scalable Probabilistic Tensor Factorization for Binary and Count Data","authors":[{"name":"Piyush Rai","slug":"piyush-rai","given":"Piyush","family":"Rai","affiliation":""},{"name":"Changwei Hu","slug":"changwei-hu","given":"Changwei","family":"Hu","affiliation":""},{"name":"Matthew Harding","slug":"matthew-harding","given":"Matthew","family":"Harding","affiliation":""},{"name":"Lawrence Carin","slug":"lawrence-carin","given":"Lawrence","family":"Carin","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2015/530","pdf":"https://ijcai.org/proceedings/2015/0530.pdf","markdown":"https://ijcai.org/proceedings/2015/530.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/530","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":530,"pages":{"first":3770,"last":3775},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}