Ruixin Yang | Department of Earth Systems and GeoInformation Sciences, GMU

Thursday October 18, 4:30 PM | Research 1 Room 301

Kernel-based principal component analysis (KPCA)

Principal Component Analysis (PCA) has been extensively used in different fields including earth science for spatial pattern identifications. However, the intrinsic linear feature associated with standard PCA prevents scientists from detecting nonlinear structures. Kernel-based principal component analysis (KPCA), a recently emerging technique, provides a new approach for exploring and identifying nonlinear patterns in scientific data. In this presentation, KPCA algorithms and its applications will be introduced. The sub-topics include recasting KPCA in the commonly used PCA notation for earth science communities and demonstrate how to apply the KPCA technique into the analysis of earth science data sets, handling the large number of principal components, computing pre-images, etc.

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