swMATH ID: 11449
Software Authors: S. K. Shevade; S. S. Keerthi
Description: A Simple and Efficient Algorithm for Gene Selection using Sparse Logistic Regression. Motivation: This paper gives a new and efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based on the Gauss–Seidel method and is asymptotically convergent. It is simple and extremely easy to implement; it neither uses any sophisticated mathematical programming software nor needs any matrix operations. It can be applied to a variety of real-world problems like identifying marker genes and building a classifier in the context of cancer diagnosis using microarray data. Results: The gene selection method suggested in this paper is demonstrated on two real-world data sets and the results were found to be consistent with the literature.
Homepage: http://drona.csa.iisc.ernet.in/~shirish/SparseLOGREG.html
Related Software: glmnet; LASSO; R; penalized; TFOCS; LIBSVM; LIBLINEAR; Pegasos; Saga; ProxSARAH; NESTA; glmpath; l1_logreg; lars; Glmnet; OWL-QN; Matrix; elasticnet; PNOPT; FTVd
Cited in: 29 Documents

Citations by Year