El Análisis de Componentes Principales con Kernel (Kernel PCA) es una extensión del Análisis de Componentes Principales (PCA) que permite manejar relaciones no lineales en los datos. Mientras que el ...

Abstract: Different color spaces are better for different applications. This paper investigates the performance of face recognition with some color spaces using kernel-based Principal Component ...

Abstract: Accurate sensing equipment for capturing human hand data is crucial for robot hand teleoperation. Individual calibration processes to reflect individual anatomical differences in human hands ...

Now, we can demonstrate that a kernel PCA using the linear kernel is the same than the old plain PCA. As before, we work with the centered dataset **X**; but this time we will not focus on its ...

This paper studies kernel PCA in a decentralized setting, where data are distributively observed with full features in local nodes, and a fusion center is prohibited. Compared with linear PCA, the use ...