Block coordinate descent algorithm improves variable selection and estimation in error-in-variables regressionestimation accuracyhigh dimensionLassomeasurement errorvariable selectionMedical research increasingly includes high-dimensional regression modeling with a need for error-in-variables methods. The Convex ...
The pseudo-code of coordinate descent method see the following figure: The following points should be noted. If coordinate descent method is used in non-smooth objective functions, then the method maybe stop to search results in some points which are not stationary (critical) points (非驻点). ...
(2013) Blondel et al. Machine Learning. Over the past decade, ℓ 1 regularization has emerged as a powerful way to learn classifiers with implicit feature selection. More recently, mixed-norm (e.g., ℓ 1/ℓ 2) regularization has been utilized as a
A Dual Coordinate Descent Method:双坐标下降法 热度: On The Convergence of Block Coordinate Descent Type Methods 热度: convergence of a block coordinate descent method for nondifferentiable minimization 热度: Block Coordinate Descent Slides are adapted based on: ...
Starting with an invalid non﹊njective initial map, ABCD behaves as a modified block coordinate descent up to the point where the current mapping is cleared of invalid simplices. Then, the algorithm converges rapidly into the chosen iterative solver. Our method is very general, fastヽonverging...
在复杂数据中变量往往成组出现,考虑了Lasso、SCAD、Bridge及MCP四种不同模型选择的惩罚项,研究了它们在群组变量中的方法及其块坐标下降算法,在Logistic模型的条件下进行模拟,结果表明Composite MCP组惩罚方法在预测能力和变量选择上均优于其他三种群组惩罚方法,并运用
The CBS formulation provides the foundation for our iterative pruning algorithm, so let’s start by taking a closer look at this algorithm first. We start by following previous work and approximating the change in loss function δL using a second-order T...
In this paper we characterize this parsimony problem, and develop a block-coordinate descent algorithm that delivers parsimonious models by sequentially estimating an additive decomposition of the transfer function of interest. Numerical simulations show the efficacy of the proposed approach. PDF Abstract...
In our work, we propose a differentially private random block coordinate descent method that selects multiple coordinates with varying probabilities in each iteration using sketch matrices. Our algorithm generalizes both DP-CD and the classical DP-SGD (Differentially Private Stochastic Gradient Descent),...
In this paper, we combine the two types of methods together and propose online randomized block coordinate descent (ORBCD). At each iteration, ORBCD only computes the partial gradient of one block coordinate of one mini-batch samples. ORBCD is well suited for the composite minimization problem...