Titlebook: Generalized Principal Component Analysis; René Vidal,Yi Ma,S.S. Sastry Textbook 2016 Springer-Verlag New York 2016 Principal component ana

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Robust Principal Component AnalysisIn the previous chapter, we considered the PCA problem under the assumption that all the sample points are drawn from the same statistical or geometric model: a low-dimensional subspace.

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Algebraic-Geometric MethodsIn this chapter, we consider a generalization of PCA in which the given sample points are drawn from an unknown arrangement of subspaces of unknown and possibly different dimensions.

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Chizobam N. Idahosa,A. Ross Kerrations, however, a linear or affine subspace may not be able to capture nonlinear structures in the data. For instance, consider the set of all images of a face obtained by rotating it about its main axis of symmetry. While all such images live in a high-dimensional space whose dimension is the numb

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Negotiations and Peace Processesseparated by salient edges or contours, and each region consists of pixels with homogeneous color or texture. In computer vision, this is widely accepted as a crucial step for any high-level vision tasks such as object recognition and understanding image semantics.

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