Sparse group lasso and high dimensional multinomial classification
Sparse group lasso and high dimensional multinomial classification
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DOI：
10.1016/j.csda.2013.06.004
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发表时间：
2014-03-01
影响因子：
1.8
通讯作者：
Hansen, Niels Richard
中科院分区：
文献类型：
--
作者：
Vincent, Martin;Hansen, Niels Richard
利用坐标梯度下降算法求解稀疏群套索优化问题。该算法适用于一类广泛的凸损失函数。建立了算法的收敛性，并用该算法对多项式稀疏群lasso分类器的性能进行了研究。在三个不同的真实数据示例中，多项式组lasso在分类错误率和包含更少的分类特征方面明显优于多项式lasso。在R包msg1中提供了多项式稀疏组lasso算法的实现。它的性能随问题规模的变化而变化，正如所考虑的一个例子所示——一个有10 k个特征的50类分类问题，这相当于估计了50万个参数。(C) 2013 Elsevier B.V.版权所有
The sparse group lasso optimization problem is solved using a coordinate gradient descent algorithm. The algorithm is applicable to a broad class of convex loss functions. Convergence of the algorithm is established, and the algorithm is used to investigate the performance of the multinomial sparse group lasso classifier. On three different real data examples the multinomial group lasso clearly outperforms multinomial lasso in terms of achieved classification error rate and in terms of including fewer features for the classification. An implementation of the multinomial sparse group lasso algorithm is available in the R package msg1. Its performance scales well with the problem size as illustrated by one of the examples considered-a 50 class classification problem with 10 k features, which amounts to estimating 500 k parameters. (C) 2013 Elsevier B.V. All rights reserved.