Name
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. |
Year | Venue | Field |
|---|---|---|
2013 | ICML | Elastic net regularization,Computer science,Generalization,Group lasso,Lasso (statistics),Algorithm,Robustness (computer science),Robust regression,Regularization (mathematics),Artificial intelligence,Machine learning,Linear regression |
DocType | Citations | PageRank |
Conference | 2 | 0.38 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenzhuo Yang | 1 | 14 | 3.02 |
| Xu, Huan | 2 | 1116 | 71.73 |