基于Fisher判别分析的有监督特征提取和星系光谱分类

随着天文观测技术的进步、数据获取能力的提高和大型光谱巡天计划的相继实施,光谱数据的自动处理研究越来越受到重视和关注。文章在分析了文献中光谱自动分类研究的特点和无监督特征提取方法所固有的一些不足的基础上指出了光谱有监督特征提取研究的必要性。并重点研究了Fisher判别分析(FDA)有监督特征提取方法在星系光谱自动分类中的应用。该方法： (1) 具有突出的维数约减能力; (2) 能有效地融合训练数据的类别信息,并按照分类能力提取特征。实验表明,将FDA方法用于某些星系细分类不仅明显地提高了分类器的速度,而且具有良好的分类性能。因此,对于较大的光谱识别系统更能体现出该方法的优越性。     

麻痹性贝毒产毒藻株的三维荧光光谱识别技术研究

利用三维荧光光谱技术,研究了以微小亚历山大藻 （台湾株）（AMSY）、塔玛亚历山大藻（大亚湾株）（ATDY）、链状裸甲藻（防城港株）（GCFC）、塔玛亚历山大藻（香港株）（ATHK）、链状亚历山大藻（南海株）（ACSY）5株4种产麻痹性贝毒(PSP)微藻以及21株不产PSP藻在不同温度条件下培养微藻的各生长期产毒能力与三维荧光的关系。研究结果表明,在不同温度条件下,产毒藻类单位藻细胞产毒量会发生显著变化,低温可促进麻痹性贝毒的产生。通过Db7小波分解,选取Ca3尺度分量的联合荧光谱作为特征谱并利用Fisher判别发现,产PSP藻与不产PSP藻荧光差异主要集中在λ_ex为400~425和450~545nm;λ_em为715~750 nm的波段,利用判别函数建立判别式,实现了对产麻痹性贝类毒素藻类的识别测定。对产PSP毒素藻类的判别率达到93.7%,不产PSP毒素藻类判别率达到93.3% ,综合判别率为93.5%。该研究可实现对水体中活体产PSP微藻的快速识别,为进一步开发产毒微藻识别仪提供理论基础。     

Operator-valued free Fisher information and modular frames

We introduce the operator-valued free Fisher information for a random variable in an operator-valued noncommutative probability space and point out its relations to the amalgamated freeness. Using M. Frank and D. Larson's modular frame notion we can construct the conjugate variable for an operator-valued semicircle variable with conditional expectation covariance. Then we obtain its free Fisher information and show it is equal to the index of the conditional expectation. At last the conjugate variable with respect to a modular frame operator for a semicircle variable is also constructed.

     

Global statistical information in exponential experiments and selection of exponential models

The concept of global statistical information in the classical statistical experiment with independent exponentially distributed samples is investigated. Explicit formulas are evaluated for common exponential families. It is shown that the generalized likelihood ratio test procedure of model selection can be replaced by a generalized information procedure. Simulations in a classical regression model are used to compare this procedure with that based on the Akaike criterion.     

Inference for earthquake models: A self-correcting model

Questions of asymptotic inference are discussed for a point process model in which the conditional intensity function increases monotonically between events and drops by determined (nonrandom) amounts after each event. Parameter estimates are shown to be consistent and, except under the null hypothesis of a Poisson process, normally distributed. Under the null hypothesis, however, the Hessian matrix is not asymptotically constant, and the limiting distribution of the likelihood ratio statistics is not χ², but has a form related to that of the Cramer-von Mises ω² statistic for the test of goodness of fit.     

Info-quantifiers’ map-characterization revisited

We highlight the potentiality of a special Information Theory (IT) approach in order to unravel the intricacies of nonlinear dynamics, the methodology being illustrated with reference to the logistic map. A rather surprising dynamic feature→plane-topography map becomes available.