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稀疏表示(SR)工具箱

于 2023-04-03 发布 文件大小:110.54 kB
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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。 ;

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