摘要
分析了稀疏表示的数学本质就是稀疏正规化约束下的信号分解,研究了一种正交匹配追踪的稀疏表示算法并利用矩阵Cholesky分解简化迭代过程中矩阵求逆计算来快速实现算法,将该算法应用在人脸识别中,利用训练样本构建冗余字典,将测试样本看成冗余字典中训练样本的线性组合,通过在不同人脸库上的实验证明了该方法的有效性。
We analyzed the mathematic essence of sparse representation,sparse regularized signal decomposition.Stu-died a sparse representation algorithm of orthogonal matching pursuit.Using the matrix Cholesky decomposition,we rea-lized the OMP algorithm a fast version.We cast the recognition problem as one of classifying among multiple linear regression models and developed a new framework from sparse signal representation.We viewed a test sample as the linearcombination of training samples.We conducted experiments on face recognition to verify the efficacy of the proposed algorithm.
出处
《计算机科学》
CSCD
北大核心
2010年第9期267-269,278,共4页
Computer Science
基金
国家自然科学基金项目(60875010)资助
关键词
稀疏表示
稀疏编码
压缩感知
正交匹配追踪
特征提取
Sparse representation
Sparse coding
Compressed sensing
Orthogonal matching pursuit
Feature extraction
