Representing Complex Data Using Localized Principal Components with Application to Astronomical Data
Analysis of simple principal component as a tool for dimension reduction can fail badly. Local approximations of principal component as among the most promising. Localized versions of component and principal curves and local partitioning algorithms.
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A method for performing a nonlinear form of Principal. Component Analysis is proposed. By the use of integral operator kernel functions one can compute principal components in high dimensional feature spaces, related to input space by some nonlinear map.
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