Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 18, Issue 6, Pages 1826-1830Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2007.900229
Keywords
generalized Least Angle Regression (LARS); Least Absolute Shrinkage and Selection Operator (LASSO); sparse logistic regression
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This letter gives an efficient algorithm for tracking the solution curve of sparse logistic regression With respect to the L I 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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