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Least squares multi-label feature selection algorithmwith label information(PDF)


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Least squares multi-label feature selection algorithmwith label information
Chen HongMa YingcangYang XiaofeiXu Qiuxia
School of Sciences,Xi’an Polytechnic University,Xi’an 710600,China
least squares sparse regularization multi label feature selection
In order to better reflect the importance of label information,based on the traditional least squares regression model,a least squares regression model containing label information was constructed to solve the multi-label feature selection problem. A slack variable ω was added to the labels one by one,so that the different classes of regression targets were moved in the opposite direction. The distances are expanded between the classes. Furthermore,combining 2,1 norms,a least squares multi-label feature selection with label information model and algorithm were proposed. Finally,the convergence of the algorithm is proved and the efficiency of the algorithm is proved by experiments.


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Last Update: 2019-09-30