Numerical examples illustrating our method's efficiency are presented for solving the LASSO problem in which the goal is to recover a sparse signal from a limited number of observations. 展开 关键词: Split feasibility problem Armijo-line search Projection operator ...
In this paper, we discuss a parsimonious approach to estimation of high-dimensional covariance matrices via the modified Cholesky decomposition with lasso. Two different methods are proposed. They are the equi-angular and equi-sparse methods. We use simulation to compare the performance of the propos...
A Modified Principal Component Technique Based on the LASSO I. T. Jolliffe and M. Uddin. A modified principal component technique based on the lasso. Journal of Computational and Graphical Statistics, 12:531-547... IT Jolliffe,TM Uddin - 《Journal of Computational & Graphical Statistics》 被引...
such as the Frobenius norm, nuclear norm, and Lasso, has been explored to enhance the accuracy of air quality prediction23,28. Lasso regularisation applies a penalty to the absolute value of regression coefficients, which reduces less
We study the asymptotic properties of Lasso+mLS and Lasso+Ridge under the\nsparse high-dimensional linear regression model: Lasso selecting predictors and\nthen modified Least Squares (mLS) or Ridge estimating their coefficients.\nFirst,... H Liu,B Yu - The Institute of Mathematical Statistics ...
Numerical examples illustrating our method's efficiency are presented for solving the LASSO problem in which the goal is to recover a sparse signal from a limited number of observations.doi:10.1007/s11590-017-1148-3AVIV GIBALILI-WEI LIU
A modified principal component technique based on the lasso. Journal of Computational and Graphical Statistics 12, 531-547.I. T. Jolliffe, N.T. Trendafilov, and M. Uddin, A modified principal component tech- nique based on the LASSO, Journal of Computational and Graphical Statistics, 12 (...
Marc Lasson (@mlasson)— contributions John Letey (@johnletey)— contributions Peng Lyu (@rebornix)— contributions Cédric Malard (@cmalard)— contributions Nguyen Long Nhat (@torn4dom4n)— contributions Aurelio Ogliari (@nobitagit)— contributions Connor Peet (@connor4312)— contributions Maxi...
Other -norm minimization models that can be reformulated into (1) include basis pursuit [8], Huber function fitting [9], group lasso [10], etc. Matrix completion: In some applications such as the movie ratings in the Netflix problem, part of the data (elements of a matrix) is unaccessib...
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