Kernel principal component analysis combining rotation forest method for linearly inseparable data

Rotation forest (RoF) is an ensemble classifier combining linear analysis theories and decision tree algorithms. In recent existing works, RoF was widely applied to various fields with outstanding performance compared to traditional machine learning techniques, given that a reasonable number of base classifiers is provided. However, the conventional RoF algorithm suffers from classifying linearly inseparable datasets. In this study, a hybrid algorithm integrating kernel principal component analysis (KPCA) and the conventional RoF algorithm is proposed to overcome the classification difficulty for linearly inseparable datasets. The radial basis function (RBF) is selected as the kernel for the KPCA method to establish the nonlinear mapping for linearly inseparable data. Moreover, we evaluate various kernel parameters for better performance. Experimental results show that our algorithm improves the performance of RoF with linearly inseparable datasets, and therefore provides higher classification accuracy rates compared with other ensemble machine learning methods.     

语言和音乐层级结构的加工

语言和音乐在呈现过程中, 小单元相互结合组成更大的单元, 最终形成层级结构。已有研究表明, 听者能够将连续的语流和音乐切分成层级结构, 并在大脑中形成层级表征。在感知基础之上, 听者还能将新出现的语言和音乐事件整合到层级结构之中, 形成连贯理解, 最终顺利地完成交流过程。未来研究应剖析边界线索在层级结构感知中的作用, 考察不同层级整合过程的影响因素, 进一步探索语言和音乐层级结构加工的关系。     

场景主旨加工及其机制

场景主旨是指观察者在一次注视场景的过程中所获得知觉和语义信息。近年来, 场景主旨加工研究已经成为视知觉领域的重要内容, 对该问题的研究将有助于揭示视觉信息加工的机制, 对智能机器视觉的研制也有一定的借鉴意义。对场景主旨加工的影响因素、争议性的问题以及场景主旨的神经基础进行评论; 未来可以在场景主旨加工的基本单元、相关的理论解释、层级加工的调节因素、注意的调节作用、时间动力特性和脑功能网络的构建等方面做进一步的探讨。     

The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite well in recovering the underlying truth but frequently end in a local minimum. In this paper we evaluate whether or not this local minimum problem can be mitigated by means of two common strategies for avoiding local minima in combinatorial data analysis: simulated annealing (SA) and use of a multistart procedure. In particular, we propose a generic SA algorithm for hierarchical classes analysis and three different types of random starts. The effectiveness of the SA algorithm and the random starts is evaluated by reanalyzing data sets of previous simulation studies. The reported results support the use of the proposed SA algorithm in combination with a random multistart procedure, regardless of the properties of the data set under study. Eva Ceulemans is a post-doctoral fellow of the Fund for Scientific Research Flanders (Belgium). Iwin Leenen is a post-doctoral researcher of the Spanish Ministerio de Educación y Ciencia (programa Ramón y Cajal). The research reported in this paper was partially supported by the Research Council of K.U. Leuven (GOA/05/04).     

Hierarchical Classes Modeling of Rating Data

Hierarchical classes (HICLAS) models constitute a distinct family of structural models for N-way N-mode data. All members of the family include N simultaneous and linked classifications of the elements of the N modes implied by the data; those classifications are organized in terms of hierarchical, if–then-type relations. Moreover, the models are accompanied by comprehensive, insightful graphical representations. Up to now, the hierarchical classes family has been limited to dichotomous or dichotomized data. In the present paper we propose a novel extension of the family to two-way two-mode rating data (HICLAS-R). The HICLAS-R model preserves the representation of simultaneous and linked classifications as well as of generalized if–then-type relations, and keeps being accompanied by a comprehensive graphical representation. It is shown to bear interesting relationships with classical real-valued two-way component analysis and with methods of optimal scaling. The research reported in this paper was supported by the Research Fund of the University of Leuven (GOA/00/02 and GOA/05/04) and by the Fund for Scientific Research-Flanders (project G.0146.06). Eva Ceulemans is a Post-doctoral Researcher supported by the Fund for Scientific Research, Flanders. The authors gratefully acknowledge the help of Gert Quintiens and Kaatje Bollaerts in collecting the data used in Section 4 and of Jan Schepers in additional analyses of these data.     

Generalized multilevel structural equation modeling

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.gllamm can be downloaded from http://www.gllamm.org. The paper was written while Sophia Rabe-Hesketh was employed at and Anders Skrondal was visiting the Department of Biostatistics and Computing, Institute of Psychiatry, King's College London.     

A Multilevel Contextual Model of Neighborhood Collective Efficacy

Different data sources were used to examine hypothesized relations among neighborhood-, family-, and individual-level variables, and perceptions of neighborhood collective efficacy. Data were from 1,105 individuals (56% female, 42% African American, and 58% White) nested within 55 neighborhoods and 392 families, analyzed within a multilevel design using a 3-level model. At the neighborhood level, the study examined relations between Census, police, and neighborhood representative indicators. At the family level, the model examined the influence of marital status and family income. At the individual level, gender and age were examined. Results indicated that age at the individual level, marital status at the family level, and poverty and perceived gang activity at the neighborhood level predicted levels of neighborhood collective efficacy. The study illustrated significant variation across neighborhoods and families, and demonstrates the utility of combining different sources of neighborhood data to examine relations of interest within a multilevel framework.     

Ultimate Questions of Science and the Theory of System Relations

Summary  Whenever an adequate theory is found in science, we will still be left with two questions: why this theory rather than some other theory, and how should this theory be interpreted? I argue that these questions can be answered by a theory of system relations. The basic idea is that fundamental characteristics of systems, viz. those arising from the general systemic nature of those systems, cannot be comprehended with the aid of discipline-specific methods. The systems theory required should commence with an analysis of the qualitatively different relations possible between systems, because it is precisely the nature of those relations that determines the basic structures of systems. That the theory of the fundamental system relations and their ontological and epistemological implications is indeed able to provide the answers sought is demonstrated in theoretical physics and Plessner’s analysis of the basic structures of plant, animal and human being.     

认知诊断中的Q矩阵及其作用

Q矩阵作为连接认知和测量的桥梁,在认知诊断中起重要作用。本文梳理了应用Q矩阵解决认知诊断相关问题的理论与方法。首先整理Q矩阵的相关概念、算法、性质及其在认知诊断中的作用;并根据Q矩阵可计算理论构念效度、可以构成格等,指出Q矩阵是特殊的关联矩阵;接着介绍Q矩阵理论研究方面的几个近期发展;并对Q矩阵未来的应用研究作出展望。期望本文能为测量工作者更灵活地利用Q矩阵提供参考和帮助。