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implements the l1l2 regularization with double optimization described and studied in [1,2]. The package is implemented in Python(≥2.5.0) and requires NumPy(≥1.3.0).

The L1L2Py homepage can be found here together with the source code.


L1L2Signature is an implementation of an unbiased framework originally thought for gene expression analysis. The framework was used in many real applications and the package is composed by a set of Python scripts and a set of useful classes and functions that could be used to manually read and/or analyze high-throughput data extending/integrating the proposed pipeline.
The L1L2Signature homepage can be found here together with the source code.


Thresholded Landweber algorithm (Lasso) (C++ class):


(Model assessment for Feature Selection) is a platform independent C++ toolbox that includes the implementation of elastic net and group LASSO for binary and multi-class classification problems. For the latter, the (groups of) features that are selected are the ones that best discriminate between all the classes simultaneously.
Our toolbox also provides model selection functionalies, allowing the user to automatically select the best performing regularization parameters. The input data file is compliant with libSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) format, to favor the interchange with classification.

The libMsFeatS homepage with code and instructions can be found here


PADDLE is a Python package for learning dictionaries with frame-like properties, as well as achieving sparse coding of the training data.
The PADDLE homepage can be found here, with the source code.


PASPAL (Proximal Algorithms for SPArse Learning) is a set of MATLAB toolboxes that implement different regularization approaches to sparse learning (lasso, elastic net, group lasso, and group lasso with overlap). Click here to visit the PASPAL page and download the code

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