A simple and efficient algorithm for gene selection using sparse logistic regression

By Shirish K. Shevade, S. Sathiya Keerthi. Bioinform., 2003

402 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/87d663379ac8c493cc988c4ab45f7ad22f14ae3e

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In Molecular Biology

1. Ata Kabán
2. Jakramate Bootkrajang
3. P. Shanti Sastry
4. Himanshu Kumar
5. Jacob Goldberger
6. Ehud Ben-Reuven

In Statistics and Probability

1. Ata Kabán
2. Jakramate Bootkrajang

Citations in the corpus

• Optimization with Sparsity-Inducing Penalties (2011, neutral): “This leads for instance to the Lasso [133] or basis pursuit [37] with the square loss and to `1-regularized logistic regression (see, for instance, [75, 127]) with the logistic loss.”
• Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection (2015, neutral): “This strategy is popular for (cid:96) 1 -regularization [Shevade and Keerthi, 2003, Wu and Lange, 2008, Li and Osher, 2009] and in general is given by [see Bertsekas, 1999, § 8.4] However, the length of the step ( (cid:107) x k +1 − x k ) could be arbitrarily small under this choice.”
• A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning (2014, neutral): “It applies in a similar manner to sparse logistic regression (Shevade and Keerthi, 2003) and, perhaps more interestingly, to Group-LASSO (Yuan and Lin, 2006).”
• Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty (2011, neutral): “This method has previously been applied for solving lasso (Friedman et al., 2007; Krishnapuram et al., 2005; Shevade and Keerthi, 2003) and elastic net optimization problems (Friedman et al.”
• L1‐regularization path algorithm for generalized linear models (2007, neutral): “As applied to regression (Tibshirani 1996, Tibshirani 1997) and classi(cid:12)cation methods (Genkin et al. 2004, Shevade & Keerthi 2003, Zhu, Rosset, Hastie & Tibshirani 2003), penalizing the size of the L 1 norm of the coe(cid:14)cients is useful because it accompanies variable selection.”
• Accelerated Proximal Gradient Methods for Nonconvex Programming (2015, neutral): “Sparse LR is widely used in areas such as bioinformatics [25] and text categorization [26].”
• L1 Penalized Estimation in the Cox Proportional Hazards Model (2009, neutral): “(1) and (2) for all values of the tuning parameters s and l, by tracing the solution of b̂ as a function of l and/or s. Optimizing Eq.”
• The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms (2008, neutral): “Similar ideas for the standard Lasso have also been introduced in (Shevade & Keerthi, 2003).”
• Classification of mislabelled microarrays using robust sparse logistic regression (2013, positive): “A vast literature demonstrates that sparsity-inducing regularisation approaches are effective in such cases (MacKay (1995); Shevade and Keerthi (2003); Cawley and Talbot (2006)).”
• Large-Scale Bayesian Logistic Regression for Text Categorization (2007, neutral): “Similar algorithms for logistic regression have been developed by Shevade and Keerthi (2003), Krishnapuram, Hartemink, Carin, and Figueiredo (2005), and others.”
• Large-scale sparse logistic regression (2009, neutral): “, Gauss-Seidel [31], grafting [28], shooting [11], BBR [8].”
• A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems (2013, neutral): “…in the literature (Tibshirani, 1996; Efron et al., 2004) and has been applied successfully to many applications including signal/image processing, biomedical in-formatics and computer vision (Shevade & Keerthi, 2003; Wright et al., 2008; Beck & Teboulle, 2009; Wright et al., 2009; Ye & Liu, 2012).”
• Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches (2007, neutral): “3 In our experiments, we used the line search of [16] for both the Gauss-Seidel and Shooting algorithm.”
• Regularization Paths for Generalized Linear Models via Coordinate Descent (2010, neutral): “Early references include Fu (1998), Shevade and Keerthi (2003) and Daubechies et al. (2004).”