SpaSM: A MATLAB Toolbox for Sparse Statistical Modeling. SpaSM is a Matlab toolbox for performing sparse regression, classification and principal component analysis. The toolbox has been developed at the Department of Informatics at the Technical University of Denmark. Development started in 2004 and the toolbox receives regular updates. The code is well documented and consists of a series of pure Matlab functions. Examples, test cases and utility functions are also included. The algorithms are based on the regularization path-following paradigm developed mainly at Stanford University
Keywords for this software
References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Mohebbi, Shima; Pamukcu, Esra; Bozdogan, Hamparsum: A new data adaptive elastic net predictive model using hybridized smoothed covariance estimators with information complexity (2019)
- Karl Sjöstrand; Line Clemmensen; Rasmus Larsen; Gudmundur Einarsson; Bjarne Ersbøll: SpaSM: A MATLAB Toolbox for Sparse Statistical Modeling (2018) not zbMATH
- Nazemi, Abdolreza; Heidenreich, Konstantin; Fabozzi, Frank J.: Improving corporate bond recovery rate prediction using multi-factor support vector regressions (2018)
- Kundu, Abhisek; Drineas, Petros; Magdon-Ismail, Malik: Recovering PCA and sparse PCA via hybrid-((\ell_1,\ell_2)) sparse sampling of data elements (2017)