Subspace Segmentation And High-Dimensional Data Analysis

This thesis developed theory and associated algorithms to solve subspace segmentation problem. Given a set of data W={w_1,...,w_N} in R^D that comes from a union of subspaces, we focused on determining a nonlinear model of the form U={S_i}_{i in I}, where S_i is a set of subspaces, that is nearest to W. The model is then used to classify W into clusters. Our first approach is based on the binary reduced row echelon form of data matrix. We prove that, in absence of noise, our approach can find the number of subspaces, their dimensions, and an orthonormal basis for each subspace S_i. We provide a comprehensive analysis of our theory and determine its limitations and strengths in presence of outliers and noise. Our second approach is based on nearness to local subspaces approach and it can handle noise effectively, but it works only in special cases of the general subspace segmentation problem (i.e., subspaces of equal and known dimensions). Our approach is based on the computation of a binary similarity matrix for the data points. A local subspace is first estimated for each data point. Then, a distance matrix is generated by computing the distances between the local subspaces and points. The distance matrix is converted to the similarity matrix by applying a data-driven threshold. The problem is then transformed to segmentation of subspaces of dimension 1 instead of subspaces of dimension d. The algorithm was applied to the Hopkins 155 Dataset and generated the best results to date

ADAPTIVE GENETIC ALGORITHMS APPLIED TO DYNAMIC MULTI-OBJECTIVE PROBLEMS

This paper describes an application of adaptive genetic algorithms in which multi-objectives such as minimizing the territory loses and maximizing enemy aircraft loses are achieved by finding the optimum war allocation scenario simulated by the THUNDER software. A genetic algorithm with two fuzzy logic based mechanisms is used. In the first mechanism, the mutation and crossover rates are changed adaptively to provide a fast and smooth convergence to the optimum possible solutions. In the second mechanism, the multi-objective fitness function coefficients are changed dynamically in each run. Comparisons of the results of the best fitness values, the adaptive genetic algorithms with dynamic fitness function improve the convergence rates considerably and maintain smoother convergence to the best possible solutions than that of the conventional genetic algorithms

The prevalence of childhood psychopathology in Turkey: a cross-sectional multicenter nationwide study (EPICPAT-T).

Aim: The aim of this study was to determine the prevalence of childhood psychopathologies in Turkey

The prevalence of childhood psychopathology in Turkey: a cross-sectional multicenter nationwide study (EPICPAT-T)

Conclusion: This is the largest and most comprehensive epidemiological study to determine the prevalence of psychopathologies in children and adolescents in Turkey. Our results partly higher than, and partly comparable to previous national and international studies. It also contributes to the literature by determining the independent predictors of psychopathologies in this age group

Prevalence of Childhood Affective disorders in Turkey: An epidemiological study

Aim: To determine the prevalence of affective disorders in Turkey among a representative sample of Turkish population. Methods: This study was conducted as a part of the "The Epidemiology of Childhood Psychopathology in Turkey" (EPICPAT-T) Study, which was designed by the Turkish Association of Child and Adolescent Mental Health. The inclusion criterion was being a student between the second and fourth grades in the schools assigned as study centers. The assessment tools used were the K-SADS-PL, and a sociodemographic form that was designed by the authors. Impairment was assessed via a 3 point-Likert type scale independently rated by a parent and a teacher. Results: A total of 5842 participants were included in the analyses. The prevalence of affective disorders was 2.5 % without considering impairment and 1.6 % when impairment was taken into account. In our sample, the diagnosis of bipolar disorder was lacking, thus depressive disorders constituted all the cases. Among depressive disorders with impairment, major depressive disorder (MDD) (prevalence of 1.06%) was the most common, followed by dysthymia (prevalence of 0.2%), adjustment disorder with depressive features (prevalence of 0.17%), and depressive disorder-NOS (prevalence of 0.14%). There were no statistically significant gender differences for depression. Maternal psychopathology and paternal physical illness were predictors of affective disorders with pervasive impairment. Conclusion: MDD was the most common depressive disorder among Turkish children in this nationwide epidemiological study. This highlights the severe nature of depression and the importance of early interventions. Populations with maternal psychopathology and paternal physical illness may be the most appropriate targets for interventions to prevent and treat depression in children and adolescents