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Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference
2010
Neural Information Processing Systems
We consider multivariate regression problems involving high-dimensional predictor and response spaces. To efficiently address such problems, we propose a variable selection method, Multivariate Group Orthogonal Matching Pursuit, which extends the standard Orthogonal Matching Pursuit technique. This extension accounts for arbitrary sparsity patterns induced by domain-specific groupings over both input and output variables, while also taking advantage of the correlation that may exist between the
dblp:conf/nips/LozanoS10
fatcat:m7tkihwy4ze6vdubh4ff2euhn4