A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis(DESN), was proposed for face recognition. Within the framework of DE...A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis(DESN), was proposed for face recognition. Within the framework of DESN, the sparse local scatter and multi-class nonparametric between-class scatter were exploited for within-class compactness and between-class separability description, respectively. These descriptions, inspired by sparse representation theory and nonparametric technique, are more discriminative in dealing with complex-distributed data. Furthermore, DESN seeks for the optimal projection matrix by simultaneously maximizing the nonparametric between-class scatter and minimizing the sparse local scatter. The use of Fisher discriminant analysis further boosts the discriminating power of DESN. The proposed DESN was applied to data visualization and face recognition tasks, and was tested extensively on the Wine, ORL, Yale and Extended Yale B databases. Experimental results show that DESN is helpful to visualize the structure of high-dimensional data sets, and the average face recognition rate of DESN is about 9.4%, higher than that of other algorithms.展开更多
针对高光谱图像存在“维数灾难”的问题,提出一种全局判别与局部稀疏保持的高光谱图像半监督特征提取算法(GLSSFE)。该算法通过LDA算法的散度矩阵保存有类标样本的全局类内判别信息和全局类间判别信息,结合利用半监督PCA算法对有类标和...针对高光谱图像存在“维数灾难”的问题,提出一种全局判别与局部稀疏保持的高光谱图像半监督特征提取算法(GLSSFE)。该算法通过LDA算法的散度矩阵保存有类标样本的全局类内判别信息和全局类间判别信息,结合利用半监督PCA算法对有类标和无类标样本进行主成分分析,保存样本的全局结构;利用稀疏表示优化模型自适应揭示样本数据间的非线性结构,将局部类间判别权值和局部类内判别权值嵌入半监督LPP 算法保留样本数据的局部结构,从而最大化同类样本的相似性和异类样本的差异性。通过1-NN和SVM两个分类器分别对Indian Pines和Pavia University 两个公共高光谱图像数据集进行分类,验证所提特征提取方法的有效性。实验结果表明,该GLSSFE算法最高总体分类精度分别达到89.10%和92.09%,优于现有的特征提取算法,能有效地挖掘高光谱图像的全局特征和局部特征,极大地提升高光谱图像的地物分类效果。展开更多
基金Project(40901216)supported by the National Natural Science Foundation of China
文摘A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis(DESN), was proposed for face recognition. Within the framework of DESN, the sparse local scatter and multi-class nonparametric between-class scatter were exploited for within-class compactness and between-class separability description, respectively. These descriptions, inspired by sparse representation theory and nonparametric technique, are more discriminative in dealing with complex-distributed data. Furthermore, DESN seeks for the optimal projection matrix by simultaneously maximizing the nonparametric between-class scatter and minimizing the sparse local scatter. The use of Fisher discriminant analysis further boosts the discriminating power of DESN. The proposed DESN was applied to data visualization and face recognition tasks, and was tested extensively on the Wine, ORL, Yale and Extended Yale B databases. Experimental results show that DESN is helpful to visualize the structure of high-dimensional data sets, and the average face recognition rate of DESN is about 9.4%, higher than that of other algorithms.
文摘针对高光谱图像存在“维数灾难”的问题,提出一种全局判别与局部稀疏保持的高光谱图像半监督特征提取算法(GLSSFE)。该算法通过LDA算法的散度矩阵保存有类标样本的全局类内判别信息和全局类间判别信息,结合利用半监督PCA算法对有类标和无类标样本进行主成分分析,保存样本的全局结构;利用稀疏表示优化模型自适应揭示样本数据间的非线性结构,将局部类间判别权值和局部类内判别权值嵌入半监督LPP 算法保留样本数据的局部结构,从而最大化同类样本的相似性和异类样本的差异性。通过1-NN和SVM两个分类器分别对Indian Pines和Pavia University 两个公共高光谱图像数据集进行分类,验证所提特征提取方法的有效性。实验结果表明,该GLSSFE算法最高总体分类精度分别达到89.10%和92.09%,优于现有的特征提取算法,能有效地挖掘高光谱图像的全局特征和局部特征,极大地提升高光谱图像的地物分类效果。
