scholarly journals Groupyr: Sparse Group Lasso in Python

2021 ◽  
Vol 6 (58) ◽  
pp. 3024
Author(s):  
Adam Richie-Halford ◽  
Manjari Narayan ◽  
Noah Simon ◽  
Jason Yeatman ◽  
Ariel Rokem
Keyword(s):  
Group Lasso ◽  
Sparse Group Lasso

Inverse Sparse Group Lasso Model for Robust Object Tracking

2017 ◽  
Vol 19 (8) ◽  
pp. 1798-1810 ◽  
Author(s):  
Yun Zhou ◽  
Jianghong Han ◽  
Xiaohui Yuan ◽  
Zhenchun Wei ◽  
Richang Hong
Keyword(s):  
Object Tracking ◽  
Group Lasso ◽  
Sparse Group Lasso

A Novel Method for Enhancing Bearing Fault Feature Based on Sparse Group Lasso

2021 ◽  
Author(s):  
Changkun Han ◽  
Wei Lu ◽  
Pengxin Wang ◽  
Liuyang Song ◽  
Huaqing Wang
Keyword(s):  
Group Lasso ◽  
Bearing Fault ◽  
Sparse Group Lasso ◽  
Feature Based ◽  
Novel Method

Rule Extraction from Decision Trees Ensembles: New Algorithms Based on Heuristic Search and Sparse Group Lasso Methods

2017 ◽  
Vol 16 (06) ◽  
pp. 1707-1727 ◽  
Author(s):  
Morteza Mashayekhi ◽  
Robin Gras
Keyword(s):  
Decision Trees ◽  
Predictive Accuracy ◽  
Weight Vector ◽  
Rule Extraction ◽  
Group Lasso ◽  
Hill Climbing ◽  
Data Sets ◽  
Sparse Group Lasso ◽  
Rule Set ◽  
Interpretable Models

Decision trees are examples of easily interpretable models whose predictive accuracy is normally low. In comparison, decision tree ensembles (DTEs) such as random forest (RF) exhibit high predictive accuracy while being regarded as black-box models. We propose three new rule extraction algorithms from DTEs. The RF[Formula: see text]DHC method, a hill climbing method with downhill moves (DHC), is used to search for a rule set that decreases the number of rules dramatically. In the RF[Formula: see text]SGL and RF[Formula: see text]MSGL methods, the sparse group lasso (SGL) method, and the multiclass SGL (MSGL) method are employed respectively to find a sparse weight vector corresponding to the rules generated by RF. Experimental results with 24 data sets show that the proposed methods outperform similar state-of-the-art methods, in terms of human comprehensibility, by greatly reducing the number of rules and limiting the number of antecedents in the retained rules, while preserving the same level of accuracy.

scholarly journals Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jin-Jia Wang ◽  
Fang Xue ◽  
Hui Li
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Group Lasso ◽  
High Dimensional ◽  
Test Accuracy ◽  
Gradient Descent Method ◽  
Feature Subset ◽  
Eeg Signals ◽  
Sparse Group Lasso ◽  
Selection Of

Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs). Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.

scholarly journals Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Jan Klosa ◽  
Noah Simon ◽  
Pål Olof Westermark ◽  
Volkmar Liebscher ◽  
Dörte Wittenburg
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Gradient Descent ◽  
Methylation Status ◽  
R Package ◽  
Group Lasso ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Sparse Group Lasso ◽  
Proximal Gradient Descent

Abstract Background Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalization approaches are often the methods of choice. They are especially useful in case of multicollinearity, which appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalized by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realized by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally, seagull -the R package presented here- produces complete regularization paths. Results Publicly available high-dimensional methylation data are used to compare seagull to the established R package SGL. The results of both packages enabled a precise prediction of biological age from DNA methylation status. But even though the results of seagull and SGL were very similar (R2 > 0.99), seagull computed the solution in a fraction of the time needed by SGL. Additionally, seagull enables the incorporation of weights for each penalized feature. Conclusions The following operators for linear regression models are available in seagull: lasso, group lasso, sparse-group lasso and Integrative LASSO with Penalty Factors (IPF-lasso). Thus, seagull is a convenient envelope of lasso variants.

scholarly journals Identification of hospital cost drivers using sparse group lasso

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0204300 ◽  
Author(s):  
Piotr Swierkowski ◽  
Adrian Barnett
Keyword(s):  
Hospital Cost ◽  
Group Lasso ◽  
Cost Drivers ◽  
Sparse Group Lasso
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