【摘要】 局部保持投影(Locality preserving projection,LPP)在特征提取中得到了广泛的应用。但是,LPP不使用数据的类别信息,并且采用L2范数来进行距离测量,对异常值高度敏感。本文从监督的角度考虑LPP的权值矩阵,并结合低秩回归的方法,提出一种新的模型来发现和提取特征。利用L2,1范数来约束损失函数和回归矩阵,不仅降低了对异常值的敏感性,而且限制了回归矩阵的低秩条件。然后给出了优化问题的求解方法。最后,本文将该方法应用于多个人脸数据库和掌纹数据集进行了性能测试,并将实验结果与现有的一些方法进行比较,结果表明该方法是有效的。更多还原

【Abstract】 Locality preserving projection(LPP)has been widely used in feature extraction. However,LPP does not use category information of data,and uses L2-norm for distance measurement,which is highly sensitive to outliers. We consider the weight matrix of LPP from a supervised perspective,and combine the method of low-rank regression to propose a new model to discover and extract features. By using L2,1-norm to constrain the loss function and the regression matrix,not only the sensitivity to outliers is reduced,but also the low-rank condition of the regression matrix is restricted. Then we propose a solution to the optimization problem. Finally,we apply the method to a series of face database and palmprint dataset to test performance,and the experimental results show that the proposed method is effective.更多还原