arXiv subjects

Yaohui Zeng

Publications and source records attributed to Yaohui Zeng.

3 recordsLinked to original sources

arXiv2017

Hybrid safe-strong rules for efficient optimization in lasso-type problems

The lasso model has been widely used for model selection in data mining, machine learning, and high-dimensional statistical analysis. However, with the ultrahigh-dimensional, large-scale data sets now collected in many real-world applications, it is important to develop algorithms to solve the lasso that efficiently scale up to problems of this size. Discarding features from certain steps of the algorithm is a powerful technique for increasing efficiency and addressing the Big Data challenge. In this paper, we propose a family of hybrid safe-strong rules (HSSR) which incorporate safe screening rules into the sequential strong rule (SSR) to remove unnecessary computational burden. In particular, we present two instances of HSSR, namely SSR-Dome and SSR-BEDPP, for the standard lasso problem. We further extend SSR-BEDPP to the elastic net and group lasso problems to demonstrate the generalizability of the hybrid screening idea. Extensive numerical experiments with synthetic and real data sets are conducted for both the standard lasso and the group lasso problems. Results show that our proposed hybrid rules can substantially outperform existing state-of-the-art rules.

stat.ML↗

arXiv2017

The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R

Penalized regression models such as the lasso have been extensively applied to analyzing high-dimensional data sets. However, due to memory limitations, existing R packages like glmnet and ncvreg are not capable of fitting lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are increasingly seen in many areas such as genetics, genomics, biomedical imaging, and high-frequency finance. In this research, we implement an R package called biglasso that tackles this challenge. biglasso utilizes memory-mapped files to store the massive data on the disk, only reading data into memory when necessary during model fitting, and is thus able to handle out-of-core computation seamlessly. Moreover, it's equipped with newly proposed, more efficient feature screening rules, which substantially accelerate the computation. Benchmarking experiments show that our biglasso package, as compared to existing popular ones like glmnet, is much more memory- and computation-efficient. We further analyze a 31 GB real data set on a laptop with only 16 GB RAM to demonstrate the out-of-core computation capability of biglasso in analyzing massive data sets that cannot be accommodated by existing R packages.

stat.CO↗

arXiv2016

Overlapping group logistic regression with applications to genetic pathway selection

Discovering important genes that account for the phenotype of interest has long been challenging in genomewide expression analysis. Analyses such as Gene Set Enrichment Analysis (GSEA) that incorporate pathway information have become widespread in hypothesis testing, but pathway-based approaches have been largely absent from regression methods due to the challenges of dealing with overlapping pathways and the resulting lack of available software. The R package grpreg is widely used to fit group lasso and other group-penalized regression models; in this study, we develop an extension, grpregOverlap, to allow for overlapping group structure using the latent variable approach proposed by Jacob et al. (2009). We compare this approach to the ordinary lasso and to GSEA using both simulated and real data. We find that incorporation of prior pathway information substantially improves the accuracy of gene expression classifiers, and we shed light on several ways in which hypothesis-testing approaches such as GSEA differ from regression approaches with respect to the analysis of pathway data.

stat.ME↗