Sparse Group Lasso: Consistency and Climate Applications

By Soumyadeep Chatterjee, Karsten Steinhaeuser, Arindam Banerjee, Snigdhansu Chatterjee, Auroop Ratan Ganguly. 2012

86 citations recorded by the source corpus.

The design of statistical predictive models for climate data gives rise to some unique challenges due to the high dimensionality and spatio-temporal nature of the datasets, which dictate that models should exhibit parsimony in variable selection. Recently, a class of methods which promote structured sparsity in the model have been developed, which is suitable for this task. In this paper, we prove theoretical statistical consistency of estimators with tree-structured norm regularizers. We consider one particular model, the Sparse Group Lasso (SGL), to construct predictors of land climate using ocean climate variables. Our experimental results demonstrate that the SGL model provides better predictive performance than the current state-of-the-art, remains climatologically interpretable, and is robust in its variable selection.

Subfields: Artificial Intelligence, Molecular Biology, Statistics and Probability

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Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, methodology): “In recent work [66], applications of sparse group Lasso were explored to model the domain characteristics of cli-”
Sparse Partially Linear Additive Models (2014, background): “, [23]) that could be applied to SPLAM.”
Bayesian Sparse Group Selection (2016, methodology): “Chatterjee et al. (2012) applied sparse group Lasso to climate data analysis.”
Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014, methodology): “However, this method can also be applied for improved understanding of the complex dependence structure between climate variables, especially in a high-dimensional setting (Chatterjee et al., 2012; Das et al., 2012, 2013).”
Machine Learning for the Geosciences: Challenges and Opportunities (2017, unknown): “Methods for handling the problem of high dimensions and small sample sizes have been explored in [53], where sparsity-inducing regularizers such as sparse group Lasso were developed to model the domain characteristics of climate variables.”