l1-penalized least squares coordinate descentNiels Richard Hansen
L1-Penalized Quantile Regression in High-Dimensional Sparse Models We consider median regression and, more generally, a possibly infinite collection of quantile regressions in high-dimensional sparse models. In these model... A Belloni,V Chernozhukov - 《Annals of Statistics》 被引量: 0发表: 2009...
Feature Selection via 1-Penalized Squared-Loss Mutual Information. Technical Report 1210.1960, arXiv, 2012.W. Jitkrittum, H. Hachiya, and M. Sugiyama, "Feature selec- tion via L1-penalized squared-loss mutual information," IEICE Transactions on Information and Systems, 2013, to appear....
摘要: Recently, the N-way partial least squares (NPLS) approach was reported as an effective tool for neuronal signal decoding and brain-computer interface (BCI) system calibration. This method...关键词: Discrete wavelet transforms genomics smoothing methods wavelet coefficients DOI: 10.1088/1741-...
It relaxes the moment matching requirement of expectation propagation by adding a relaxation factor into the KL minimization. We penalize this relaxation with a l1 penalty. As a result, when two distributions in the relaxed KL divergence are similar, the relaxation factor will be penalized to ze...
High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence Pradeep Ravikumar† Martin J.Wainwright†,♯pradeepr@stat.b..
IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data 来自 EBSCO 喜欢 0 阅读量: 150 作者: Boulesteix Anne-Laure, De Bin Riccardo, Jiang Xiaoyu, Fuchs Mathias 摘要: As modern biotechnologies advance, it has become increasingly frequent that ...
L1Packv2 is a Mathematica package that contains a number of algorithms that can be used for the minimization of an ℓ1-penalized least squares functional. The algorithms can handle a mix of penalized and unpenalized variables. Several instructive examples are given. Also, an implementation that...
Concave penalized least squares estimation (PLSE) takes advantage of signal strength to reduce this bias, leading to sharper error bounds in prediction, coefficient estimation and variable selection. For prediction a... L Feng,CH Zhang - 《Annals of Statistics》 被引量: 1发表: 2017年 grpSLOPE...
Group SLOPE is a penalized linear regression method that is used for adaptive selection of groups of significant predictors in a high-dimensional linear model. A unique feature of the Group SLOPE method is that it offers (group) false discovery rate control (i.e., control of the expected prop...