The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R.

IF 4.3 3区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Journal of Machine Learning Research Pub Date : 2015-03-01

Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu

引用次数: 0

Abstract

This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, ℓ _q Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME). These methods exploit different nonsmooth loss functions to gain modeling exibility, estimation robustness, and tuning insensitiveness. The developed solver is based on the alternating direction method of multipliers (ADMM), which is further accelerated by the multistage screening approach. The package flare is coded in double precision C, and called from R by a user-friendly interface. The memory usage is optimized by using the sparse matrix output. The experiments show that flare is efficient and can scale up to large problems.

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用于 R 中高维线性回归和精度矩阵估计的 flare 软件包。

本文介绍了一个名为 flare 的 R 软件包，它实现了一系列新的高维回归方法（LAD Lasso、SQRT Lasso、ℓ q Lasso 和 Dantzig selector）及其对稀疏精度矩阵估计的扩展（TIGER 和 CLIME）。这些方法利用不同的非光滑损失函数来获得建模的可扩展性、估计的鲁棒性和调整的不敏感性。开发的求解器基于交替方向乘法（ADMM），并通过多级筛选方法进一步加速。软件包 flare 采用双精度 C 语言编码，并通过友好的用户界面从 R 语言调用。通过使用稀疏矩阵输出，优化了内存使用。实验表明，flare 是高效的，可以扩展到大型问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Machine Learning Research 工程技术-计算机：人工智能

CiteScore

18.80

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.