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Stochastic Conjugate Gradient Algorithm with Variance Reduction
Oct 16, 2018
Conjugate gradient (CG) methods are a class of important methods for solving linear equations and nonlinear optimization problems. In this paper, we propose a new stochastic CG algorithm with variance reduction and we prove its linear convergence with the Fletcher and Reeves method for strongly convex and smooth functions. We experimentally demonstrate that the CG with variance reduction algorithm converges faster than its counterparts for four learning models, which may be convex, nonconvex or nonsmooth. In addition, its area under the curve performance on six large-scale data sets is comparable to that of the LIBLINEAR solver for the L2-regularized L2-loss but with a significant improvement in computational efficiency
* 10 pages, 4 figures, appeared in IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, CGVR algorithm is available on github: https://github.com/xbjin/cgvr
Linear NDCG and Pair-wise Loss
Mar 11, 2013
Linear NDCG is used for measuring the performance of the Web content quality assessment in ECML/PKDD Discovery Challenge 2010. In this paper, we will prove that the DCG error equals a new pair-wise loss.