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处理lasso regression。
使用stochastic Markov property。-Algorithm for solving the Lasso problem:
0.5* (y- X*beta) *(y- X*beta)+ lambda* ||beta||_1
where ||beta||_1 is the L_1 norm i.e., ||beta||_1 = sum(abs( beta ))
We use the method proposed by Fu et. al based on single co-ordinate
descent. For more details see GP s notes or the following paper:
Penalized Regressions: The Bridge Versus the Lasso
Wenjiang J. FU, Journal of Computational and Graphical Statistics,
Volume 7, Number 3, Pages 397?416, 1998
使用stochastic Markov property。-Algorithm for solving the Lasso problem:
0.5* (y- X*beta) *(y- X*beta)+ lambda* ||beta||_1
where ||beta||_1 is the L_1 norm i.e., ||beta||_1 = sum(abs( beta ))
We use the method proposed by Fu et. al based on single co-ordinate
descent. For more details see GP s notes or the following paper:
Penalized Regressions: The Bridge Versus the Lasso
Wenjiang J. FU, Journal of Computational and Graphical Statistics,
Volume 7, Number 3, Pages 397?416, 1998
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下载文件列表
estimateLassoLambda.m
exampleLassoUsage.m
normalize.m
solveLasso.m