Sparsity and Smoothness Via the Fused Lasso

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ID: 290247
2004
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Ranked #137 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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Abstract
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 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.
Reference Key
openalex_W2140514146 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Robert Tibshirani, Michael A. Saunders, Saharon Rosset, Ji Zhu, Keith Knight
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 2004
DOI
10.1111/j.1467-9868.2005.00490.x
URL
https://doi.org/10.1111/j.1467-9868.2005.00490.x
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