The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations. We extend the group lasso to logistic regression models and present an efficient algorithm, that is especially suitable for high dimensional problems, w...

Scalars based on linear and generalized models are commonly used in fatality disease predictions. Currently, standard approaches for variable selection not explored well with high dimensional data. This manuscript proposed a new method logistics inspiration from heterogeneous ensemble methods. The goal of this study is to extend classical variables methods such as stepwise, lasso, or RF the cas...

PURPOSE To measure progression of the visual field (VF) mean deviation (MD) index in longitudinal 10-2 VFs more accurately, by adding information from 24-2 VFs using Lasso regression. METHODS A training dataset consisted of 138 eyes from 97 patients with glaucoma or ocular hypertension and a testing dataset consisted of 40 eyes from 34 patients with glaucoma or ocular hypertension. The Lasso ...

The lasso [19] and group lasso [23] are popular algorithms in the signal processing and statistics communities. In signal processing, these algorithms allow for efficient sparse approximations of arbitrary signals in overcomplete dictionaries. In statistics, they facilitate efficient variable selection and reliable regression under the linear model assumption. In both cases, there is now ample ...

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been done on assessing univariate associations between gene expression profiles with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable select...

SVRG and its variants are among the state of art optimization algorithms for the large scale machine learning problem. It is well known that SVRG converges linearly when the objective function is strongly convex. However this setup does not include several important formulations such as Lasso, group Lasso, logistic regression, among others. In this paper, we prove that, for a class of statistic...

Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow quick monitoring nutrients. However, at present, obtaining accurate estimates proves to be difficult due weak spectral features low accuracy estimation models. This study proposed ne...