Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/85866
Title: A unified robust regression model for Lasso-like algorithms
Authors: Yang, W.
Xu, H. 
Issue Date: 2013
Citation: Yang, W.,Xu, H. (2013). A unified robust regression model for Lasso-like algorithms. 30th International Conference on Machine Learning, ICML 2013 (PART 2) : 1622-1630. ScholarBank@NUS Repository.
Abstract: We develop a unified robust linear regression model and show that it is equivalent to a general regularization framework to encourage sparse-like structure that contains group Lasso and fused Lasso as specific examples. This provides a robustness interpretation of these widely applied Lasso-like algorithms, and allows us to construct novel generalizations of Lasso-like algorithms by considering different uncertainty sets. Using this robustness interpretation, we present new sparsity results, and establish the statistical consistency of the proposed regularized linear regression. This work extends a classical result from Xu et al. (2010) that relates standard Lasso with robust linear regression to learning problems with more general sparse-like structures, and provides new robustness-based tools to to understand learning problems with sparse-like structures. Copyright 2013 by the author(s).
Source Title: 30th International Conference on Machine Learning, ICML 2013
URI: http://scholarbank.nus.edu.sg/handle/10635/85866
Appears in Collections:Staff Publications

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