Sparsity and smoothness via the fused lasso

  title={Sparsity and smoothness via the fused lasso},
  author={Martin Saunders and Saharon Rosset and Ji Zhu},
The lasso (Tibshirani 1996) 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, that is, having many coefficients equal to zero. Here we propose the “fused lasso”, a generalization of the lasso designed for problems with features that can be ordered in some meaningful way. The fused lasso penalizes both the L1 norm of the coefficients and their successive differences. Thus it encourages both… CONTINUE READING
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Adaptable, efficient and robust methods for regression and classification via piecewise linear regularized coefficient paths

  • S. Rosset, J. Zhu
  • 2003
1 Excerpt

Asymptotics for lasso-type estimators

  • K. Knight, W. Fu
  • Annals of Statistics
  • 2000
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