… Organization: We introduce sparse logistic regression in Section 2, review the Nesterov’s
method in Section 3, derive an adaptive line search scheme in Section 4, present the …

… secure largescale sparse logistic regression model and … sparse matrix multiplication
protocol by combining HE and SS. Finally, we present the secure large-scale logistic regression …

… the only effort that aims to obtain sparse KLR. It proposed an … In this paper, we address the
challenge of large-scale sparse … of training examples, leading to sparse kernel classifiers and …

… for largescale data processing and analytics. In this work, we consider a distributed Newton
method for solving logistic regression as … Because we consider sparse storage, intermediate …

… a new model selection which produces a sparse solution by approaching the ridge solution.
… We also report the degree of sparsity for each model. The sparsity is given by:(13) s = 1 - …

… Large-scale sparse logistic regression. In Proceedings of the 15th ACM SIGKDD international
conference on Knowledge discovery and data mining, pages 547–556. ACM, 2009. …

… At first, the construction of the search direction in Newton or quasi-Newton is very time and
memory consuming for large-scale problems. Secondly, the line search of the open domain …

… sparse multinomial logistic regression (SMLR) [10], which inherits the sparse solution of sparse
logistic regression and … deal with large-scale samples and large-scale features datasets. …

… In Section 2, we present the iterative shrinkage algorithm for sparse logistic regression,
and prove its global convergence and Q-linear convergence. In Section 3, we provide the …

… Our first implementation of the Lasso to conditional logistic regression was based … logistic
regression and the partial likelihood of stratified, discrete-time Cox proportional hazards model (…