中文说明：应用背景这个工具箱包括机器学习方法：基于稀疏编码的分类，基于字典的降维子字典学习，学习模型，线性回归和分类（LRC）。核l_1正则或（和）非负约束稀疏编码和字典学习模型在这个工具箱实现。 ；关键技术活动集，内点，近端，和分解方法来优化这些模型。目前的版本是1.9（2015年3月2日）。这个工具箱是免费的学术用途。 ；
English Description：
Application backgroundThis toolbox includes the machine learning approaches: sparse coding based classification, dictionary learning based dimension reduction, sub-dictionary learning models, and linear regression classification (LRC). Kernel l_1 regularized or (and) non-negative constrained sparse coding and dictionary learning models are implemented in this toolbox. Key TechnologyActive-set, interior-point, proximal, and decomposition methods are provided to optimize these models. The current version is 1.9 (March 02, 2015). This toolbox is free for academic usage.