sparse-regression

A package for the sparse identification of nonlinear dynamical systems from data

Efficient Algorithms for L0 Regularized Learning

a collection of modern sparse (regularized) linear regression algorithms.

Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)

Statistical Models with Regularization in Pure Julia

MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3

Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian learning, and basis pursuit.

Matlab toolbox for sparse regression

Sequential adaptive elastic net (SAEN) approach, complex-valued LARS solver for weighted Lasso/elastic-net problems, and sparsity (or model) order detection with an application to single-snapshot source localization.

locus R package - Large-scale variational inference for variable selection in sparse multiple-response regression

Variable Selection and Task Grouping for Multi-Task Learning (VSTG-MTL)

Nonconvex Exterior Point Operator Splitting

MGLM Toolbox for Matlab

Knowledge elicitation when the user can give feedback to different features of the model with the goal to improve the prediction on the test data in a "smal n, large p" setting.

STELA algorithm for sparsity regularized linear regression (LASSO)

Sparse Identification of Truncation Errors (SITE) for Data-Driven Discovery of Modified Differential Equations

Generalized Orthogonal Least-Squares in CUDA

Black-box spike and slab variational inference, example with linear models

Robust regression algorithm that can be used for explaining black box models (R implementation)

Hybrid Approach to Sparse Group Fused Lasso

Robust regression algorithm that can be used for explaining black box models (Python implementation)

Automatic hyperparameter selection for Lasso-like models solving the M/EEG source localization problem

Physically-informed model discovery of systems with nonlinear, rational terms using the SINDy-PI method. Contains functionality for spectral filtering/differentiation.

This work presents the application of machine learning models in order to obtain a sparse governing equation of complex fluid dynamics problems.

The Method of Entropic Regression, sparse system identification method based on causality inference of complex networks.

Physics-informed refinement learning for equation discovery

SparseStep: Approximating the Counting Norm for Sparse Regularization

The Python Implementation of Sparse Regression.

Assignment: Linear and Sparse Regression Consider the attached dataset about advertising and sales. The attributes denote the investments on advertising in TV, radio etc and the target variable is the total sales. The aim is to predict the sales from the investments on advertising. 1) Randomly divide the dataset into training (75%) and testing (25%) subsets [1 mark] 2) Using Linear Regression, fit a model to predict the sales from investments using your own formula. Compare the coefficients as found by the python library function [3 marks] 3) Compute the mean squared error on the testing set [1 marks] 4) Using ridge regression with different values of lambda (0.5, 1, 5, 10, 50, 100) plot the coefficients against each other, and also compare the test set mean squared errors. [3 marks] 5) Use the library function of "LASSO regression" to find out which of the 3 features is most important, i.e. whose coefficient is furthest from 0