2015 LongitudinalLASSOJointlyLearnin

• (Xu et al., 2015) ⇒ Tingyang Xu, Jiangwen Sun, and Jinbo Bi. (2015). “Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783403

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Cited By

• http://scholar.google.com/scholar?q=%222015%22+Longitudinal+LASSO%3A+Jointly+Learning+Features+and+Temporal+Contingency+for+Outcome+Prediction
• http://dl.acm.org/citation.cfm?id=2783258.2783403&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; gradient methods; longitudinal modeling; regression; regularization methods; sparse predictive modeling

Abstract

Longitudinal analysis is important in many disciplines, such as the study of behavioral transitions in social science. Only very recently, feature selection has drawn adequate attention in the context of longitudinal modeling. Standard techniques, such as generalized estimating equations, have been modified to select features by imposing sparsity-inducing regularizers. However, they do not explicitly model how a dependent variable relies on features measured at proximal time points. Recent graphical Granger modeling can select features in lagged time points but ignores the temporal correlations within an individual's repeated measurements. We propose an approach to automatically and simultaneously determine both the relevant features and the relevant temporal points that impact the current outcome of the dependent variable. Meanwhile, the proposed model takes into account the non-i.i.d nature of the data by estimating the within-individual correlations. This approach decomposes model parameters into a summation of two components and imposes separate block-wise LASSO penalties to each component when building a linear model in terms of the past τ measurements of features. One component is used to select features whereas the other is used to select temporal contingent points. An accelerated gradient descent algorithm is developed to efficiently solve the related optimization problem with detailed convergence analysis and asymptotic analysis. Computational results on both synthetic and real world problems demonstrate the superior performance of the proposed approach over existing techniques.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 LongitudinalLASSOJointlyLearnin	Jiangwen Sun Jinbo Bi Tingyang Xu			Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction				10.1145/2783258.2783403		2015