Análise de Agrupamentos Com Base na Teoria da Informação: Uma Abordagem Representativa

Currently, one of the biggest challenges for the field of data mining is to perform cluster analysis on complex data. Several techniques have been proposed but, in general, they can only achieve good results within specific areas providing no consensus of what would be the best way to group this kind of data. In general, these techniques fail due to non-realistic assumptions about the true probability distribution of the data. Based on this, this thesis proposes a new measure based on Cross Information Potential that uses representative points of the dataset and statistics extracted directly from data to measure the interaction between groups. The proposed approach allows us to use all advantages of this information-theoretic descriptor and solves the limitations imposed on it by its own nature. From this, two cost functions and three algorithms have been proposed to perform cluster analysis. As the use of Information Theory captures the relationship between different patterns, regardless of assumptions about the nature of this relationship, the proposed approach was able to achieve a better performance than the main algorithms in literature. These results apply to the context of synthetic data designed to test the algorithms in specific situations and to real data extracted from problems of different fields

Contribuições aos Processos de Clustering com Base em Métricas não-Euclidianas

In this work we present a new clustering method that groups up points of a data set in classes. The method is based in a algorithm to link auxiliary clusters that are obtained using traditional vector quantization techniques. It is described some approaches during the development of the work that are based in measures of distances or dissimilarities (divergence) between the auxiliary clusters. This new method uses only two a priori information, the number of auxiliary clusters Na and a threshold distance dt that will be used to decide about the linkage or not of the auxiliary clusters. The number os classes could be automatically found by the method, that do it based in the chosen threshold distance dt, or it is given as additional information to help in the choice of the correct threshold. Some analysis are made and the results are compared with traditional clustering methods. In this work different dissimilarities metrics are analyzed and a new one is proposed based on the concept of negentropy. Besides grouping points of a set in classes, it is proposed a method to statistical modeling the classes aiming to obtain a expression to the probability of a point to belong to one of the classes. Experiments with several values of Na e dt are made in tests sets and the results are analyzed aiming to study the robustness of the method and to consider heuristics to the choice of the correct threshold. During this work it is explored the aspects of information theory applied to the calculation of the divergences. It will be explored specifically the different measures of information and divergence using the Rényi entropy. The results using the different metrics are compared and commented. The work also has appendix where are exposed real applications using the proposed method

Separação cega de fontes lineares e não lineares usando algoritmo genético, redes neurais artificiais RBF e negentropia de Rényi como medida de independência

Conventional methods to solve the problem of blind source separation nonlinear, in general, using series of restrictions to obtain the solution, often leading to an imperfect separation of the original sources and high computational cost. In this paper, we propose an alternative measure of independence based on information theory and uses the tools of artificial intelligence to solve problems of blind source separation linear and nonlinear later. In the linear model applies genetic algorithms and Rényi of negentropy as a measure of independence to find a separation matrix from linear mixtures of signals using linear form of waves, audio and images. A comparison with two types of algorithms for Independent Component Analysis widespread in the literature. Subsequently, we use the same measure of independence, as the cost function in the genetic algorithm to recover source signals were mixed by nonlinear functions from an artificial neural network of radial base type. Genetic algorithms are powerful tools for global search, and therefore well suited for use in problems of blind source separation. Tests and analysis are through computer simulations

On rich modal logics

I thank to my advisor, João Marcos, for the intellectual support and patience that devoted me along graduate years. With his friendship, his ability to see problems of the better point of view and his love in to make Logic, he became a great inspiration for me. I thank to my committee members: Claudia Nalon, Elaine Pimentel and Benjamin Bedregal. These make a rigorous lecture of my work and give me valuable suggestions to make it better. I am grateful to the Post-Graduate Program in Systems and Computation that accepted me as student and provided to me the propitious environment to develop my research. I thank also to the CAPES for a 21 months fellowship. Thanks to my research group, LoLITA (Logic, Language, Information, Theory and Applications). In this group I have the opportunity to make some friends. Someone of them I knew in my early classes, they are: Sanderson, Haniel and Carol Blasio. Others I knew during the course, among them I’d like to cite: Patrick, Claudio, Flaulles and Ronildo. I thank to Severino Linhares and Maria Linhares who gently hosted me at your home in my first months in Natal. This couple jointly with my colleagues of student flat Fernado, Donátila and Aline are my nuclear family in Natal. I thank my fiancée Luclécia for her precious a ective support and to understand my absence at home during my master. I thank also my parents Manoel and Zenilda, my siblings Alexandre, Paulo and Paula.Without their confidence and encouragement I wouldn’t achieve success in this journey. If you want the hits, be prepared for the misses Carl Yastrzemski

Medida de uma codificação de estímulos sonoros por potenciais pós-sinápticos e potenciais de campo local usando teoria da informação

Coding process is a fundamental aspect of cerebral functioning. The sensory stimuli transformation in neurophysiological responses has been a research theme in several areas of Neuroscience. One of the most used ways to measure a neural code e ciency is by the use of Information Theory measures, such as mutual information. Using these tools, recent studies show that in the auditory cortex both local eld potentials (LFPs) and action potential spiking times code information about sound stimuli. However, there are no studies applying Information Theory tools to investigate the e ciency of codes that use postsynaptics potentials (PSPs), alone and associated with LFP analysis. These signals are related in the sense that LFPs are partly created by joint action of several PSPs. The present dissertation reports information measures between PSP and LFP responses obtained in the primary auditory cortex of anaesthetized rats and auditory stimuli of distinct frequencies. Our results show that PSP responses hold information about sound stimuli in comparable levels and even greater than LFP responses. We have also found that PSPs and LFPs code sound information independently, since the joint analysis of these signals did neither show synergy nor redundancy.

Uso de correntropia na generalização de funções cicloestacionárias e aplicações para a extração de características de sinais modulados

Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and the Spectral Cyclic Density (SCD) can be used to extract second-order cyclostationary information. However, second-order cyclostationary information is poor in nongaussian signals, as the cyclostationary analysis in this case should comprise higher-order statistical information. This paper proposes a new mathematical tool for the higher-order cyclostationary analysis based on the correntropy function. Specifically, the cyclostationary analysis is revisited focusing on the information theory, while the Cyclic Correntropy Function (CCF) and Cyclic Correntropy Spectral Density (CCSD) are also defined. Besides, it is analytically proven that the CCF contains information regarding second- and higher-order cyclostationary moments, being a generalization of the CAF. The performance of the aforementioned new functions in the extraction of higher-order cyclostationary characteristics is analyzed in a wireless communication system where nongaussian noise exists.