Kernelized inner product-based discriminant analysis for interval data
| Autor: | Marcus Costa de Araújo, Francisco José A. Cysneiros, Renata M. C. R. de Souza, Diego C. F. Queiroz |
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| Rok vydání: | 2017 |
| Předmět: |
Graph kernel
020205 medical informatics business.industry Feature vector Pattern recognition 02 engineering and technology Linear discriminant analysis Symbolic data analysis Kernel (linear algebra) Artificial Intelligence Polynomial kernel Radial basis function kernel 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Kernel Fisher discriminant analysis business Mathematics |
| Zdroj: | Pattern Analysis and Applications. 21:731-740 |
| ISSN: | 1433-755X 1433-7541 |
| DOI: | 10.1007/s10044-017-0601-3 |
| Popis: | This work presents an approach based on the kernelized discriminant analysis to classify symbolic interval data in nonlinearly separable problems. It is known that the use of kernels allows to map implicitly data into a high-dimensional space, called feature space; computing projections in this feature space results in a nonlinear separation in the input space that is equivalent to linear separating function in the feature space. In this work, the kernel matrix is obtained based on kernelized interval inner product. Experiments with synthetic interval data sets and an application with a Brazilian thermographic breast database demonstrate the usefulness of this approach. |
| Databáze: | OpenAIRE |
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