scholarly journals Integrative analysis and variable selection with multiple high-dimensional data sets

Biostatistics ◽  
2011 ◽  
Vol 12 (4) ◽  
pp. 763-775 ◽  
Author(s):  
S. Ma ◽  
J. Huang ◽  
X. Song
Keyword(s):  
Variable Selection ◽  
High Dimensional Data ◽  
Integrative Analysis ◽  
High Dimensional ◽  
Data Sets

LASSO-Type Variable Selection Methods for High-Dimensional Data

2013 ◽  
Vol 444-445 ◽  
pp. 604-609
Author(s):  
Guang Hui Fu ◽  
Pan Wang
Keyword(s):  
Variable Selection ◽  
High Dimensional Data ◽  
Real Data ◽  
Oracle Property ◽  
High Dimensional ◽  
Data Sets ◽  
Group Effect ◽  
Selection Methods ◽  
Variable Selection Method ◽  
Type Variable

LASSO is a very useful variable selection method for high-dimensional data , But it does not possess oracle property [Fan and Li, 200 and group effect [Zou and Hastie, 200. In this paper, we firstly review four improved LASSO-type methods which satisfy oracle property and (or) group effect, and then give another two new ones called WFEN and WFAEN. The performance on both the simulation and real data sets shows that WFEN and WFAEN are competitive with other LASSO-type methods.

scholarly journals An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures

2017 ◽  
Author(s):  
Sahir Rai Bhatnagar ◽  
Yi Yang ◽  
Budhachandra Khundrakpam ◽  
Alan C Evans ◽  
Mathieu Blanchette ◽  
...  
Keyword(s):  
Variable Selection ◽  
Dimension Reduction ◽  
High Dimensional Data ◽  
Predictive Modelling ◽  
Original Data ◽  
High Dimensional ◽  
Data Sets ◽  
Imaging Studies ◽  
Modelling Framework ◽  
Network Properties

AbstractPredicting a phenotype and understanding which variables improve that prediction are two very challenging and overlapping problems in analysis of high-dimensional data such as those arising from genomic and brain imaging studies. It is often believed that the number of truly important predictors is small relative to the total number of variables, making computational approaches to variable selection and dimension reduction extremely important. To reduce dimensionality, commonly-used two-step methods first cluster the data in some way, and build models using cluster summaries to predict the phenotype.It is known that important exposure variables can alter correlation patterns between clusters of high-dimensional variables, i.e., alter network properties of the variables. However, it is not well understood whether such altered clustering is informative in prediction. Here, assuming there is a binary exposure with such network-altering effects, we explore whether use of exposure-dependent clustering relationships in dimension reduction can improve predictive modelling in a two-step framework. Hence, we propose a modelling framework called ECLUST to test this hypothesis, and evaluate its performance through extensive simulations.With ECLUST, we found improved prediction and variable selection performance compared to methods that do not consider the environment in the clustering step, or to methods that use the original data as features. We further illustrate this modelling framework through the analysis of three data sets from very different fields, each with high dimensional data, a binary exposure, and a phenotype of interest. Our method is available in the eclust CRAN package.

Mahalanobis distance informed by clustering

2018 ◽  
Vol 8 (2) ◽  
pp. 377-406
Author(s):  
Almog Lahav ◽  
Ronen Talmon ◽  
Yuval Kluger
Keyword(s):  
Mahalanobis Distance ◽  
High Dimensional Data ◽  
Hidden Variables ◽  
Real Data ◽  
Risk Groups ◽  
High Dimensional ◽  
Data Sets ◽  
Kaplan Meier ◽  
Data Points ◽  
Survival Plot

Abstract A fundamental question in data analysis, machine learning and signal processing is how to compare between data points. The choice of the distance metric is specifically challenging for high-dimensional data sets, where the problem of meaningfulness is more prominent (e.g. the Euclidean distance between images). In this paper, we propose to exploit a property of high-dimensional data that is usually ignored, which is the structure stemming from the relationships between the coordinates. Specifically, we show that organizing similar coordinates in clusters can be exploited for the construction of the Mahalanobis distance between samples. When the observable samples are generated by a nonlinear transformation of hidden variables, the Mahalanobis distance allows the recovery of the Euclidean distances in the hidden space. We illustrate the advantage of our approach on a synthetic example where the discovery of clusters of correlated coordinates improves the estimation of the principal directions of the samples. Our method was applied to real data of gene expression for lung adenocarcinomas (lung cancer). By using the proposed metric we found a partition of subjects to risk groups with a good separation between their Kaplan–Meier survival plot.

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