Recently, image feature extraction algorithms based on 2D discriminant local preserving projection (2DDLPP) algorithms have been successfully applied in many fields. The 2DDLPP can maintain the discrimination information of the local intrinsic manifold structure using two-dimensional image representation data. However, the 2DDLPP algorithm encounters the problem of the sensitivity of overlapping points (outliers) and requires high computational cost in real-world applications. In order to resolve the problems mentioned above, we introduce a new elastic feature extraction algorithm called the sparse fuzzy 2D discriminant local preserving projection (SF2DDLPP). First, the membership matrix is calculated using the fuzzy k-nearest neighbours (FKNN), which is applied to the intraclass weighted matrix and the interclass weighted matrix. Second, two theorems are developed to directly solve the generalized eigenfunctions. Finally, the optimal sparse fuzzy 2D discriminant projection matrix is regressed using the elastic net regression. The experiments show the effectiveness and stability of this algorithm on several face (ORL, Yale, AR and Yale B), USPS and palm print datasets.

近来，基于二维判别局部保留投影（2DDLPP）算法的图像特征提取算法已成功应用于许多领域。2DDLPP可以使用二维图像表示数据来维护局部固有流形结构的判别信息。但是，2DDLPP算法遇到重叠点（异常值）的敏感性问题，并且在实际应用中需要很高的计算成本。为了解决上述问题，我们引入了一种新的弹性特征提取算法，称为稀疏模糊2D判别局部保留投影（SF2DDLPP）。首先，使用模糊k最近邻（FKNN）计算隶属度矩阵，将其应用于类内加权矩阵和类间加权矩阵。第二，建立了两个定理以直接求解广义特征函数。最后，使用弹性净回归。实验表明，该算法在多个人脸（ORL，Yale，AR和Yale B），USPS和掌纹数据集上的有效性和稳定性。