2013 FastStructureLearninginGenerali

• (Bahadori et al., 2013) ⇒ Mohammad Taha Bahadori, Yan Liu, and Eric P. Xing. (2013). “Fast Structure Learning in Generalized Stochastic Processes with Latent Factors.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487578

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Fast+Structure+Learning+in+Generalized+Stochastic+Processes+with+Latent+Factors
• http://dl.acm.org/citation.cfm?id=2487575.2487578&preflayout=flat#citedby

Quotes

Author Keywords

• Generalized linear models; information search and retrieval; latent factors; time series analysis

Abstract

Understanding and quantifying the impact of unobserved processes is one of the major challenges of analyzing multivariate time series data. In this paper, we analyze a flexible stochastic process model, the generalized linear auto-regressive process(GLARP) and identify the conditions under which the impact of hidden variables appears as an additive term to the evolution matrix estimated with the maximum likelihood. In particular, we examine three examples, including two popular models for count data, i.e, Poisson and Conwey-Maxwell Poisson vector auto-regressive processes, and one powerful model for extreme value data, i.e., Gumbel vector auto-regressive processes. We demonstrate that the impact of hidden factors can be separated out via convex optimization in these three models. We also propose a fast greedy algorithm based on the selection of composite atoms in each iteration and provide a performance guarantee for it. Experiments on two synthetic datasets, one social network dataset and one climatology dataset demonstrate the superior performance of our proposed models.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 FastStructureLearninginGenerali	Eric P. Xing Yan Liu Mohammad Taha Bahadori			Fast Structure Learning in Generalized Stochastic Processes with Latent Factors				10.1145/2487575.2487578		2013