Saharon Rosset, Ji Zhu, et al.
JMLR
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 L 1 -norm of both the coefficients and their successive differences. Thus it encourages sparsity of the coefficients and also sparsity of their differences - i.e. local constancy of the coefficient profile. The fused lasso is especially useful when the number of features p is much greater than N, the sample size. The technique is also extended to the 'hinge' loss function that underlies the support vector classifier. We illustrate the methods on examples from protein mass spectroscopy and gene expression data.
Saharon Rosset, Ji Zhu, et al.
JMLR
Laxmi Parida, Marta Melé, et al.
Journal of Computational Biology
Trevor Hastie, Saharon Rosset, et al.
NeurIPS 2004
Doron M. Behar, Ene Metspalu, et al.
PLoS ONE