2012 ActiveLearningforOnlineBayesian

• (Silva & Carin, 2012) ⇒ Jorge Silva, and Lawrence Carin. (2012). “Active Learning for Online Bayesian Matrix Factorization.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339584

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Active+Learning+for+Online+Bayesian+Matrix+Factorization
• http://dl.acm.org/citation.cfm?id=2339530.2339584&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; matrix factorization; online learning

Abstract

The problem of large-scale online matrix completion is addressed via a Bayesian approach. The proposed method learns a factor analysis (FA) model for large matrices, based on a small number of observed matrix elements, and leverages the statistical model to actively select which new matrix entries / observations would be most informative if they could be acquired, to improve the model; the model inference and active learning are performed in an online setting. In the context of online learning, a greedy, fast and provably near-optimal algorithm is employed to sequentially maximize the mutual information between past and future observations, taking advantage of submodularity properties. Additionally, a simpler procedure, which directly uses the posterior parameters learned by the Bayesian approach, is shown to achieve slightly lower estimation quality, with far less computational effort. Inference is performed using a computationally efficient online variational Bayes (VB) procedure. Competitive results are obtained in a very large collaborative filtering problem, namely the Yahoo ! Music ratings dataset.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 ActiveLearningforOnlineBayesian	Lawrence Carin Jorge Silva			Active Learning for Online Bayesian Matrix Factorization				10.1145/2339530.2339584		2012