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2021 |
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| 2. | Yasmani Fernández; Iris Marrufo; Marcelo Páez; A. C. Umaquinga-Criollo; P. D. Rosero; D. H. Peluffo-Ordónez Overview on kernels for least-squares support-vector-machine-based clustering: Explaining kernel spectral clustering Journal Article In: REVISTA INVESTIGACION OPERACIONAL, vol. 42, no. 1, pp. 113-123, 2021, ISBN: 0257-4306. Abstract | Links | BibTeX | Etiquetas: Clustering, kernel principal component analysis WKPC., Kernel spectral clustering KSC, Support Vector Machine (SVM) @article{fernandez2021,ABSTRACT This letter presents an overview on some remarkable basics on kernels as well as the formulation of a clustering approach based on least-squares support vector machines. Specifically, the method known as kernel spectral clustering (KSC) is of interest. We explore the links between KSC and a weighted version of kernel principal component analysis (WKPCA). Also, we study the solution of the KSC problem by means of a primal-dual scheme. All mathematical developments are carried out following an entirely matrix formulation. As a result, in addition to the elegant KSC formulation, important insights and hints about the use and design of kernel-based approaches for clustering are provided. En esta investigación se presenta una descripción general de algunos conceptos básicos e importantes sobre kernels, así como la formulación de un enfoque de agrupación basado en máquinas de vectores de soporte de mínimos cuadrados. Específicamente, el método conocido como kernel espectral clustering (KSC) es de interés. Exploramos los enlaces entre KSC y una versión ponderada del análisis de componentes principales del núcleo (WKPCA). Además, estudiamos la solución del problema KSC por medio de un esquema primal-dual. Todos los desarrollos matemáticos se llevan a cabo siguiendo una formulación completamente matricial. Como resultado, además de una formulación de KSC, se proporcionan ideas y sugerencias importantes sobre el uso y el diseño de enfoques basados en el kernels para clustering. |
2019 |
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| 1. | Emil Vega-Gualán; Andrés Vargas; Miguel Becerra; Ana Umaquinga; Jaime A. Riascos; Diego Peluffo Brain Informatics, vol. BI 2019, no. 12, 12th International Conference Springer, Cham, 2019, ISBN: 978-3-030-37078-7, ( Print ISBN 978-3-030-37077-0 Online ISBN 978-3-030-37078-7 ). Abstract | Links | BibTeX | Etiquetas: Electroencephalogram (EEG) Epilepsy diagnosis, K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM) @conference{vega2019,Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The framework uses a well-known epilepsy dataset and runs several experiments for two and three classification problems. The results suggest that DWT decomposition with SVM is the most suitable combination. |