Sparsity and smoothness via the fused lasso

@inproceedings{Tibshirani2005SparsityAS,
  title={Sparsity and smoothness via the fused lasso},
  author={Robert Tibshirani and Michael A. Saunders and Saharon Rosset and Junhui Zhu and Keith Shelburne Knight},
  year={2005}
}
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 coefficients equal to 0). We propose the ‘fused lasso’, a generalization that is designed for problems with features that can be ordered in some meaningful way. The fused lasso penalizes the L1-norm of both the coefficients and their successive differences. Thus it encourages sparsity of the coefficients and also… CONTINUE READING
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References

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SHOWING 1-10 OF 22 REFERENCES

Sparsity and smoothness via the fused

R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, K. Knight
  • centroids of gene expression. Proc. Natn. Acad. Sci. USA,
  • 2004

Adaptable, efficient and robust methods for regression and classification via piecewise

S. Rosset, J. Zhu
  • cancer. Lancet,
  • 2003
VIEW 1 EXCERPT