GLASSOFAST: An efficient GLASSO implementation
نویسندگان
چکیده
The GLASSO algorithm has been proposed by Friedman, Hastie and Tibshirani in 2008 to solve the `1 regularized inverse covariance matrix estimation problem. The conditional dependency structure which is captured by the inverse of the covariance matrix is of interest in numerous applications and the publication of GLASSO has spurred the development of several other algorithms aimed to solve the same optimization problem. In this paper, we present GLASSOFAST, our efficient implementation of GLASSO and demonstrate via numerical experiments that it is a magnitude faster than the original implementation.
منابع مشابه
A Communication-Efficient Parallel Method for Group-Lasso
Group-Lasso (gLasso) identifies important explanatory factors in predicting the response variable by considering the grouping structure over input variables. However, most existing algorithms for gLasso are not scalable to deal with large-scale datasets, which are becoming a norm in many applications. In this paper, we present a divide-andconquer based parallel algorithm (DC-gLasso) to scale up...
متن کاملThe Graphical Lasso: New Insights and Alternatives
The graphical lasso [5] is an algorithm for learning the structure in an undirected Gaussian graphical model, using ℓ1 regularization to control the number of zeros in the precision matrix Θ = Σ-1 [2, 11]. The R package GLASSO [5] is popular, fast, and allows one to efficiently build a path of models for different values of the tuning parameter. Convergence of GLASSO can be tricky; the converge...
متن کاملConvex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso
We study a simple linear regression problem for grouped variables; we are interested in methods which jointly perform estimation and variable selection, that is, that automatically set to zero groups of variables in the regression vector. The Group Lasso (GLasso), a well known approach used to tackle this problem which is also a special case of Multiple Kernel Learning (MKL), boils down to solv...
متن کاملFunctional Graphical Models *
Graphical models have attracted increasing attention in recent years, especially in settings involving high dimensional data. In particular Gaussian graphical models are used to model the conditional dependence structure among p Gaussian random variables. As a result of its computational efficiency the graphical lasso (glasso) has become one of the most popular approaches for fitting high dimen...
متن کاملGene Network Reconstruction by Integration of Prior Biological Knowledge
With the development of high-throughput genomic technologies, large, genome-wide datasets have been collected, and the integration of these datasets should provide large-scale, multidimensional, and insightful views of biological systems. We developed a method for gene association network construction based on gene expression data that integrate a variety of biological resources. Assuming gene ...
متن کامل