Sparse Graph Embedding Based on the Fuzzy Set for Image Classification
Sparse Graph Embedding Based on the Fuzzy Set for Image Classification
复制标题
基于模糊集的稀疏图嵌入图像分类
DOI：
10.1155/2021/6638985
复制
发表时间：
2021
期刊：
影响因子：
2.3
通讯作者：
Guowei Yang
中科院分区：
文献类型：
--
作者：
Minghua Wan;Mengting Ge;Tianming Zhan;Zhangjing Yang;Hao Zheng;Guowei Yang
近年来，针对线性和非线性数据提出了许多人脸图像特征提取和降维算法，如基于局部的图嵌入算法或模糊集算法。然而，上述算法对于人脸图像不是非常有效，因为它们总是受到数据库中的重叠（离群点）和稀疏点的影响。针对这些问题，提出了一种新的有效的人脸识别降维方法--稀疏图嵌入模糊集图像分类方法。该算法的目的是利用局部图嵌入和模糊k-最近邻构造两个新的模糊拉普拉斯散射矩阵。最后通过加入弹性网络回归得到最优的鉴别稀疏投影矩阵。在UCI葡萄酒数据集和ORL、Yale、AR标准人脸数据库上的实验结果表明，该算法比其他算法更有效。
In recent years, many face image feature extraction and dimensional reduction algorithms have been proposed for linear and nonlinear data, such as local-based graph embedding algorithms or fuzzy set algorithms. However, the aforementioned algorithms are not very effective for face images because they are always affected by overlaps (outliers) and sparsity points in the database. To solve the problems, a new and effective dimensional reduction method for face recognition is proposed—sparse graph embedding with the fuzzy set for image classification. The aim of this algorithm is to construct two new fuzzy Laplacian scattering matrices by using the local graph embedding and fuzzy k-nearest neighbor. Finally, the optimal discriminative sparse projection matrix is obtained by adding elastic network regression. Experimental results and analysis indicate that the proposed algorithm is more effective than other algorithms in the UCI wine dataset and ORL, Yale, and AR standard face databases.