The study investigated the relationship among three cognitive styles and summarization abilities. Both summarization products and processes were examined. Summarizing products were scored and a canonical correlation analysis was performed to determine their relationship with three cognitive styles. Summarizing processes were examined by videotaping students as they provided think aloud protocols. Their processes were recorded on composing style sheets and analyzed qualitatively. Subjects were sixth-grade students in self-contained classes in a suburban school district. Summarizing products were collected over a two week period in the fall. Summarizing processes were collected over an eight week period in the spring of the same school year. The results of the summarizing products analysis suggest that cognitive styles are related to summarization abilities. Two canonical correlations among the two variable sets were statistically significant at the .05 level of significance (.33 and .29). The results further suggest that students who are field independent, reflective, and flexible in their attentional style may be more adept at organizing their ideas and using written mechanics while summarizing. Students who are impulsive and constricted in attentional style may exhibit strength in expressing their ideas while summarizing. Results of the summarizing processes analysis suggest that students of one cognitive style combination may exhibit different behaviors while summarizing than those of other cognitive style combinations. Students who are field independent, reflective, and flexible in their attentional style seem to display more mature, interactive behaviors while summarizing than their peers of other cognitive style combinations.

Psychopathology typically is a lasting condition that is persistent from childhood to adulthood. Therefore, it is imperative that children with health conditions and comorbid psychiatric disorders are treated for both conditions as they are likely to have a significant negative impact on children's overall health-related quality of life (HRQL). More specifically, it is important to identify the variables that affect HRQL in children with epilepsy. Research has shown that biomedical variables such as seizure severity and frequency have only moderate relationships with HRQL; therefore, additional factors, such as depression and anxiety, must be identified so that they also may be a focus of treatment. The purpose of this study was to ascertain the relationship among seizure-related variables, health-related quality of life, and psychopathology (i.e., anxiety and depression) in children with epilepsy (n = 51). The seizure-related variables examined in this study include type of seizure, seizure frequency, and seizure treatment with anti-epileptic drugs (AEDs). Canonical correlation analyses indicated that self-report and parent report of anxiety and depression were most strongly correlated with HRQL. Additionally, seizure frequency and number of anti-epileptic drugs also were correlated with HRQL. It is hoped that results from this study will inform both the medical and psychosocial treatment children with epilepsy receive. This comprehensive care needs to go beyond simply attempting to control seizures with minimal adverse drug reactions. Results from this study will contribute to the literature underscoring the importance of identifying, diagnosing, and treating children with epilepsy who have comorbid psychopathology so that they have the best possible psychosocial outcomes.

Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works. This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution. Two BCIs are presented in this thesis. In the first BCI, the subject wasable to balance a virtual inverted pendulum by thinking of activating theleft or right hand or resting. In the second BCI, the subject in the MRscanner was able to communicate with a person outside the MR scanner,through a virtual keyboard. A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.

The objective of this master thesis is to identify “key-drivers” embedded in customer satisfaction data. The data was collected by a large transportation sector corporation during five years and in four different countries. The questionnaire involved several different sections of questions and ranged from demographical information to satisfaction attributes with the vehicle, dealer and several problem areas. Various regression, correlation and cooperative game theory approaches were used to identify the key satisfiers and dissatisfiers. The theoretical and practical advantages of using the Shapley value, Canonical Correlation Analysis and Hierarchical Logistic Regression has been demonstrated and applied to market research.

Modern data collection methods are now frequently returning observations that should be viewed as the result of digitized recording or sampling from stochastic processes rather than vectors of finite length. In spite of great demands, only a few classification methodologies for such data have been suggested and supporting theory is quite limited. The focus of this dissertation is on discrimination and classification in this infinite dimensional setting. The methodology and theory we develop are based on the abstract canonical correlation concept of Eubank and Hsing (2005), and motivated by the fact that Fisher's discriminant analysis method is intimately tied to canonical correlation analysis. Specifically, we have developed a theoretical framework for discrimination and classification of sample paths from stochastic processes through use of the Loeve-Parzen isomorphism that connects a second order process to the reproducing kernel Hilbert space generated by its covariance kernel. This approach provides a seamless transition between the finite and infinite dimensional settings and lends itself well to computation via smoothing and regularization. In addition, we have developed a new computational procedure and illustrated it with simulated data and Canadian weather data.

Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2004. / Ajay Misra, Committee Member ; James Craig, Committee Member ; Richard Neu, Committee Member ; Daniel Schrage, Committee Member ; Dimitri Mavris, Committee Chair. Vita. Includes bibliographical references.

Title from title page of PDF (University of Missouri--St. Louis, viewed March 23, 2010). Includes bibliographical references (p. 352-361).

abstract: Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms. / Dissertation/Thesis / Ph.D. Computer Science 2011

Made available in DSpace on 2014-06-11T19:31:33Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-05-12Bitstream added on 2014-06-13T20:02:18Z : No. of bitstreams: 1 oliveira_jrt_dr_botfca.pdf: 1336973 bytes, checksum: 44cf547c7955ffc9a72eb93ed415128f (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A necessidade de entender o relacionamento entre variáveis biológicas faz da análise multivariada uma metodologia com grande potencial de aplicação em várias áreas do conhecimento. Na agricultura, sua utilização vem auxiliando a compreensão e a obtenção de respostas altamente interessantes e práticas, que permitem optar pelo seu emprego, tanto pela eficiência como pela acurácia do método na interpretação dos resultados. A partir da utilização de técnicas multivariadas pautadas em procedimentos quantitativos mais robustos e sensíveis, buscou-se caracterizar o perfil produtivo e climático das microrregiões do Sudeste do estado de Mato Grosso e construir modelos para quantificar e aprofundar a interrelação entre produtividade e variáveis climáticas nas respectivas regiões. Para classificar microrregiões semelhantes segundo suas características, quando nenhuma suposição foi feita concernente ao número de grupos ou a estrutura do grupo, utilizou-se a análise de agrupamento. Buscando variáveis agrícolas e de produtividade e a incorporação de novos procedimentos multivariados na interrelação desses indicadores, utilizou-se a análise de correlação canônica. Para a operacionalização desses procedimentos multivariados foram estabelecidas técnicas para estimar os componentes climáticos não disponíveis em algumas das microrregiões estudadas. A análise de agrupamento permitiu desenhar um mosaico de heterogeneidade espacial e estabelecer diferentes perfis na composição dos grupos de microrregião, reunindo as mais tradicionais no cultivo de uma espécie, ou mais produtivas, ou aquelas mais propícias ao desenvolvimento de determinada cultura. A análise fatorial estabeleceu dois eixos canônicos para as interrelações entre as culturas, sendo o primeiro fator explicando 42,22% da variância total correlacionado com as culturas anuais, podendo... / The knowledge of the relationship between biological variables makes the multivariate analysis a potential tool for applications in several science fields. In agriculture, this technique has enabled the understanding and obtaining responses very real, which show the possibility of use by both the efficiency and the accuracy of the method in the interpretation of results. The purpose of this research is to use of multivariate techniques based on quantitative procedures to improve the knowledge about the climatic variables of the southeast of Mato Grosso state, which helps to solve problems in the agricultural sector. Also, the grouping analysis classified the micro regions in similar groups. The factorial analysis showed the dimensions of the variation structure of data, enabling the determination of the extent of each variable in each dimension. The smaller regions were defined from interpreting of the interrelationship between the products grown in the region. The correlation canonic analysis was used to describe the association between the number of variables and agricultural productivity. Thus, new procedures were incorporated in multivariate interrelationship of these indicators. Some climatic components, not available in a few micro regions, were estimated through multivariate techniques. Cluster analysis allowed the design of a mosaic of spatial heterogeneity regions. It established different profiles in the composition of groups, joining the more traditional in the culture of a species, or more productive, or those for the development of a particular culture. The factorial analysis established two canonical axes for the interrelationships between cultures. The first factor explaining 42.22% of total variance associated with annual crops (called annual crops factor ). The second factor explained 16, 11% (semi perennial crop factor)... (Complete abstract click electronic access below)

There were two major goals of this study - to examine validity of scores for the Boholst Life Position Scale and to examine potential associations between life positions and affective traits. Two hundred seventy-seven students enrolled in undergraduate psychology classes at a large university volunteered for the study. Concurrent validity of scores for the life position scale was supported based on two compared instruments. Pearson product-moment correlations for the comparisons were -.765 and .617, both statistically significant at the p < .001 level. Factor analysis demonstrated that the scale could accurately be conceptualized as consisting of two factors - an "I" factor and a "You" factor. MANOVA, ANOVA, multiple linear regression, and canonical correlation analysis were used to examine associations between life positions and the affective traits of angry, sad, glad, social anxiety, loneliness, and satisfaction with life. Subjects were catagorized into four groups representing their life position: "I'm OK, you're OK," "I'm OK, you're not OK," "I'm not OK, you're OK," and "I'm not OK, you're not OK." A MANOVA employing life position as the independent variable with four levels and the six affective traits as the dependent variables demonstrated statistical significance (p < .001 level) and h2 was .505. All six separate ANOVAs, with life position as the independent variable and each separate affective trait as the dependent variable, revealed statistical significance (p < .001) and h2 varied from a high of .396 for the sadness variable to a low of .116 for social anxiety. Six separate multiple linear regression equations using two independent variables, a measure of self-esteem and a measure of the perceived OK-ness of others, and each separate affective trait as the dependent variable, showed statistical significance (p < .001). The average Adjusted R2 was .475. Both canonical correlation functions were statistically significant (Rc12 = .77 and Rc22 = .21). In summary, life positions were strongly associated with specific affective traits.