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Sparsity and smoothness via the fused lasso
Summary. The lasso penalizes a least squares regression by the sum of the absolute values (L1-norm) of the coefficients. The form of this penalty encourages sparse solutions (with many coefficientsExpand
Multi-class AdaBoost ∗
A new algorithm is proposed that naturally extends the original AdaBoost algorithm to the multiclass case without reducing it to multiple two-class problems and is extremely easy to implement and is highly competitive with the best currently available multi-class classification methods. Expand
Sparse permutation invariant covariance estimation
The paper proposes a method for constructing a sparse estima- tor for the inverse covariance (concentration) matrix in high-dimensional settings. The estimator uses a penalized normal likelihoodExpand
1-norm Support Vector Machines
The standard 2-norm SVM is known for its good performance in two-class classification. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over theExpand
The Entire Regularization Path for the Support Vector Machine
An algorithm is derived that can fit the entire path of SVM solutions for every value of the cost parameter, with essentially the same computational cost as fitting one SVM model. Expand
Kernel Logistic Regression and the Import Vector Machine
It is shown that the IVM not only performs as well as the SVM in two-class classification, but also can naturally be generalized to the multiclass case, and provides an estimate of the underlying probability. Expand
Partial Correlation Estimation by Joint Sparse Regression Models
It is shown that space performs well in both nonzero partial correlation selection and the identification of hub variables, and also outperforms two existing methods. Expand
Piecewise linear regularized solution paths
We consider the generic regularized optimization problem β(λ) = argminβ L(y, Xβ) + λJ(β). Efron, Hastie, Johnstone and Tibshirani [Ann. Statist. 32 (2004) 407-499] have shown that for the LASSO-thatExpand
L1-Norm Quantile Regression
Classical regression methods have focused mainly on estimating conditional mean functions. In recent years, however, quantile regression has emerged as a comprehensive approach to the statisticalExpand
Joint estimation of multiple graphical models.
An estimator forGaussian graphical models appropriate for data from several graphical models that share the same variables and some of the dependence structure is developed, aiming to preserve the common structure, while allowing for differences between the categories. Expand