2015 LongitudinalLASSOJointlyLearnin

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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.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2015 LongitudinalLASSOJointlyLearninTingyang Xu
Jiangwen Sun
Jinbo Bi
Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction10.1145/2783258.27834032015
AuthorTingyang Xu +, Jiangwen Sun + and Jinbo Bi +
doi10.1145/2783258.2783403 +
proceedingsProceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +
titleLongitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction +
year2015 +