EVENTO
Multi-Class Discriminant Analysis Based on Support Vector Machine Ensembles
Tipo de evento: Defesa de Tese de Doutorado
Many areas such as pattern recognition and analysis of image databases require the managing of datasets originally represented in high dimensional spaces. Besides, the original data representation implies, in general, in redundancy and noise. Thus, we must compute a more suitable feature space, reducing both the dimension and redundancy of representation as well as minimizing the computational cost of further operations. Once a feature space has been defined there is the necessity of determining the most important discriminant features for pattern recognition tasks, like classification. Discriminant analysis techniques, which in the literature are known as discriminant functions, seek to solve this type of problem. Thus, the goal of the proposed thesis is to develop discriminant analysis methods for multi-class problems. The key idea is to combine N classifiers to form a global discriminant function, which allows to rank the components of the space according to the importance of each feature to the classification problem. To achieve this goal, we use separate hyperplanes computed by traditional support vector machines (SVMs) or tangent to decision boundaries yielded by Kernel SVM (KSVM), and use the ensemble methodology known as AdaBoost.M2 to combine the linear classifiers. In this case, our proposed techniques seek to generate multiclass versions of the Discriminant Principal Component Analysis (DPCA), which was originally proposed for binary problems. In this work, principal components analysis (PCA), Convolutional neural networks (CNNs) and texture descriptors, are used to create feature space that serve as input to discriminant analysis algorithms. In terms of application for validation of the proposed techniques our focus are human face and texture images obtained from granite tiles. Further works will be undertaken by exploring color images, tensor subspaces as well as to improve performance.
Data Início: 17/05/2019 Hora: 10:00 Data Fim: 17/05/2019 Hora: 14:00
Local: LNCC - Laboratório Nacional de Computação Ciêntifica - Auditorio A
Aluno: Tiene Andre Filisbino - LNCC - LNCC
Orientador: Gilson Antônio Giraldi - Laboratório Nacional de Computação Científica - LNCC
Participante Banca Examinadora: Aura Conci - Universidade Federal Fluminense - UFF/IC Fabio André Machado Porto - Laboratório Nacional de Computação Científica - LNCC Gilson Antônio Giraldi - Laboratório Nacional de Computação Científica - LNCC Raul Queiroz Feitosa - PUC/RJ - PUC/RJ
Suplente Banca Examinadora: Artur Ziviani - Laboratório Nacional de Computação Científica - LNCC Fernando Von Zuben - - UNICAMP
