A Fast Tracking Algorithm for Generalized LARS/LASSO
IEEE TRANSACTIONS ON NEURAL NETWORKS（2007）
摘要
This letter gives an efficient algorithm for tracking the solution curve of sparse logistic regression with respect to the regularization parameter. The algorithm is based on approximating the logistic regression loss by a piecewise quadratic function, using Rosset and Zhu's path tracking algorithm on the approximate problem, and then applying a correction to get to the true path. Application of the algorithm to text classification and sparse kernel logistic regression shows that the algorithm is efficient.
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关键词
sparse logistic regression,approximate problem,piecewise quadratic function,regularization parameter,true path,efficient algorithm,logistic regression loss,solution curve,path tracking algorithm,sparse kernel logistic regression,generalized lars,lasso,index terms— sparse logistic regression,fast tracking algorithm,symmetric matrices,logistics,computer science,vectors,kernel,text analysis,logistic regression,indexing terms,application software,regression analysis,automation,classification algorithms
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