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通过交替最小化估计稀疏高斯图模型AI 解读

Several methods have recently been proposed for estimating sparse Gaussian graphical models using |$\ell_{1}$|-regularization on the inverse covariance or precision matrix. Despite recent advances, contemporary applications require even faster methods to handle ill-conditioned high-dimensional datasets. In this paper, we propose a new method for solving the sparse inverse covariance estimation problem using the alternating minimization algorithm, which effectively works as a…

Lasso 回归：通过 Gibbs 抽样的极限实现估计与收缩AI 解读

Summary The application of the lasso is espoused in high dimensional settings where only a small number of the regression coefficients are believed to be non-zero (i.e. the solution is sparse). Moreover, statistical properties of high dimensional lasso estimators are often proved under the assumption that the correlation between the predictors is bounded. In this vein, co-ordinatewise methods, which are the most common means of computing the lasso solution, naturally work…