Existing estimators of the central mean space are known to have uneven performances across different types of link functions. By combining the strength of the ordinary least squares and the principal Hessian direction...Existing estimators of the central mean space are known to have uneven performances across different types of link functions. By combining the strength of the ordinary least squares and the principal Hessian directions, the authors propose a new hybrid estimator that successfully recovers the central mean space for a wide range of link functions. Based on the new hybrid estimator, the authors further study the order determination procedure and the marginal coordinate test. The superior performance of the hybrid estimator over existing methods is demonstrated in extensive simulation studies.展开更多
The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high...The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high- dimensional spectral measurements are organized by the affinity graph where each node in this graph only connects to its local neighbors and each edge in this graph represents local similarity information. By normalizing the affinity graph appropriately, the diffusion operator of the underlying hyperspectral imagery is well-defined, which means that the Markov random walk can be simulated on the hyperspectral imagery. Therefore, the diffusion geometric coordinates, derived from the eigenfunctions and the associated eigenvalues of the diffusion operator, can capture the intrinsic geometric information of the hyperspectral imagery well, which gives more enhanced representation results than traditional linear methods, such as principal component analysis based methods. For large-scale full scene hyperspectral imagery, by exploiting the backbone approach, the computation complexity and the memory requirements are acceptable. Experiments also show that selecting suitable symmetrization normalization techniques while forming the diffusion operator is important to hyperspectral imagery representation.展开更多
选用对数去除率(log-reduction,LR)坐标研究非医疗保健产品菌落总数的辐照灭菌剂量设定问题。以具有标准抗性分布(Standard distribution of resistances,SDR)的菌落总数为研究对象,选择剂量范围从0-1至0-20k Gy,剂量间隔从1至10 k Gy的...选用对数去除率(log-reduction,LR)坐标研究非医疗保健产品菌落总数的辐照灭菌剂量设定问题。以具有标准抗性分布(Standard distribution of resistances,SDR)的菌落总数为研究对象,选择剂量范围从0-1至0-20k Gy,剂量间隔从1至10 k Gy的共56种实验剂量点选取方案,按照传统方法拟合出每个实验方案下的菌落总数"D10值",并计算出LR坐标下相应的灭菌剂量曲线。计算结果表明,传统线性拟合"D10值"法设定菌落总数灭菌剂量的有效范围为LR≤LRmax,其中LRmax为增量剂量实验中最大剂量点Dmax对应的LR值。在此有效范围内,采用最大剂量斜率Dmax/LRmax设定的菌落总数灭菌剂量比传统线性拟合"D10值"法设定的灭菌剂量更接近理论值。最大剂量斜率法与传统方法相比可以大大减少实验次数,并且所设定的菌落总数灭菌剂量更加准确,可以减少不必要的过量辐照,是对传统方法的一大改进。展开更多
文摘Existing estimators of the central mean space are known to have uneven performances across different types of link functions. By combining the strength of the ordinary least squares and the principal Hessian directions, the authors propose a new hybrid estimator that successfully recovers the central mean space for a wide range of link functions. Based on the new hybrid estimator, the authors further study the order determination procedure and the marginal coordinate test. The superior performance of the hybrid estimator over existing methods is demonstrated in extensive simulation studies.
基金The National Key Technologies R & D Program during the 11th Five-Year Plan Period (No.2006BAB15B01)
文摘The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high- dimensional spectral measurements are organized by the affinity graph where each node in this graph only connects to its local neighbors and each edge in this graph represents local similarity information. By normalizing the affinity graph appropriately, the diffusion operator of the underlying hyperspectral imagery is well-defined, which means that the Markov random walk can be simulated on the hyperspectral imagery. Therefore, the diffusion geometric coordinates, derived from the eigenfunctions and the associated eigenvalues of the diffusion operator, can capture the intrinsic geometric information of the hyperspectral imagery well, which gives more enhanced representation results than traditional linear methods, such as principal component analysis based methods. For large-scale full scene hyperspectral imagery, by exploiting the backbone approach, the computation complexity and the memory requirements are acceptable. Experiments also show that selecting suitable symmetrization normalization techniques while forming the diffusion operator is important to hyperspectral imagery representation.
文摘选用对数去除率(log-reduction,LR)坐标研究非医疗保健产品菌落总数的辐照灭菌剂量设定问题。以具有标准抗性分布(Standard distribution of resistances,SDR)的菌落总数为研究对象,选择剂量范围从0-1至0-20k Gy,剂量间隔从1至10 k Gy的共56种实验剂量点选取方案,按照传统方法拟合出每个实验方案下的菌落总数"D10值",并计算出LR坐标下相应的灭菌剂量曲线。计算结果表明,传统线性拟合"D10值"法设定菌落总数灭菌剂量的有效范围为LR≤LRmax,其中LRmax为增量剂量实验中最大剂量点Dmax对应的LR值。在此有效范围内,采用最大剂量斜率Dmax/LRmax设定的菌落总数灭菌剂量比传统线性拟合"D10值"法设定的灭菌剂量更接近理论值。最大剂量斜率法与传统方法相比可以大大减少实验次数,并且所设定的菌落总数灭菌剂量更加准确,可以减少不必要的过量辐照,是对传统方法的一大改进。
