Principal component analysis (PCA) is one of the most popular techniques for processing, compressing, and visualizing data, although its effective- ness is limited by its global linearity. While nonlinear variant of PCA have been proposed, an alternative paradigm is to capture data complex- ity by a combination of local linear PCA projections. However, concen- tional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models.
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